statistical perspective of covid-19 and analysis data...

STATISTICAL PERSPECTIVE OF COVID-19 AND ANALYSIS Data as of 3rd July 2020

Sylvain Goulet, FCIA, FSA, MAAA — ECKLER LTD. Christine Finlay, FCIA, FSA, MAAA — ECKLER LTD. Ronald Richman, FIA, FASSA, CERA — QED ACTUARIES AND CONSULTANTS

Prepared by:

SYLVAIN GOULET, FCIA, FSA, MAAA — ECKLER LTD.

Affiliate Member of the Institute and Faculty of Actuaries

Member of the Caribbean Actuarial Association

CHRISTINE FINLAY, FCIA, FSA, MAAA — ECKLER LTD.

Member of the Caribbean Actuarial Association

RONALD RICHMAN, FIA, FASSA, CERA — QED ACTUARIES AND CONSULTANTS

T A B L E O F C O N T E N T S

1. DISCLOSURE AND RESTRICTIONS OF USE 2 ..........................................................................................................

2. SCOPE OF THIS REPORT AND INTENDED USE 3 ....................................................................................................

2.1. Data Source, Reliance, Limitations and Responsibility 3 ............................................................................................................

2.2. Intended Users of This Report 3 .....................................................................................................................................................

2.3. Purpose of This Report 3 ..................................................................................................................................................................

2.4. Assumptions and Methodology, and Rationale 4 .........................................................................................................................

2.5. Use of this Report 4 ..........................................................................................................................................................................

3. EXECUTIVE SUMMARY 5 .............................................................................................................................................

4. INTRODUCTION 8 .........................................................................................................................................................

4.1. Case Fatality Rate 10 ........................................................................................................................................................................

4.2. The Ultimate Risk 13 .........................................................................................................................................................................

4.3. Some of the Other Risks 15 .............................................................................................................................................................

5. RELIABILITY OF DATA AND PROJECTIONS 16 .........................................................................................................

6. CLUSTERING ANALYSIS 19 ..........................................................................................................................................

6.1. Analysis 1 19 .......................................................................................................................................................................................

6.2. Analysis 2 22 .....................................................................................................................................................................................

7. CORRELATION ANALYSIS 24 ......................................................................................................................................

7.1. Infection Rates and Case Fatality Rate 24 ....................................................................................................................................

7.2. Infection Rates and Testing Rates 25 .............................................................................................................................................

8. COUNTRY TO COUNTRY ANALYSIS 26 .....................................................................................................................

9. SIGNS OF SLOWING DOWN OR ACCELERATING — INFECTION SPEED 33 ........................................................

9.1. Introduction 33 ..................................................................................................................................................................................

9.2. Some Key Countries 35 ....................................................................................................................................................................

TOC Page ( ) — COVID-19 Statistics and Analysisi

10. THE AMERICAS 38 ........................................................................................................................................................

10.1. Infection Speed in the Americas 44 ...............................................................................................................................................

10.2. Emphasis on the US States 45 ........................................................................................................................................................

10.3. States Comparison 50 ......................................................................................................................................................................

10.4. Emphasis on the Canadian Provinces 51 .......................................................................................................................................

10.5. Provinces Comparison 54 ................................................................................................................................................................

11. THE CARIBBEAN 55 ......................................................................................................................................................

11.1. Emphasis on the Caribbean 61 ........................................................................................................................................................

12. EUROPE 62 .....................................................................................................................................................................

12.1. Emphasis on Europe — 1 67 .............................................................................................................................................................

12.2. Emphasis on Europe — 2 68 ............................................................................................................................................................

12.3. Emphasis on Europe — 3 69 ............................................................................................................................................................

13. ASIA (INCLUDING THE MIDDLE EAST and RUSSIA) 70 ..........................................................................................

13.1. Emphasis on Asia 75 .........................................................................................................................................................................

14. AFRICA 77 .......................................................................................................................................................................

14.1. Emphasis on Africa 83 ......................................................................................................................................................................

15. OCEANIA 84 ...................................................................................................................................................................

16. GLOBAL COMPARISON OF RATES OF INFECTION 85 ............................................................................................

16.1. A Quick Overview of the World 85 .................................................................................................................................................

16.2. The Emerging Giants 86 ..................................................................................................................................................................

17. TIMELINE OF COVID-19 BY CONTINENT 87 .............................................................................................................

18. AGE-SPECIFIC AND GENDER-SPECIFIC CASES 91 ..................................................................................................

19. AGE-SPECIFIC AND GENDER-SPECIFIC “RATES” OF INFECTION 95 ...................................................................

20. AGE-SPECIFIC AND GENDER-SPECIFIC “RATES” OF DEATH 99 ..........................................................................

TOC Page ( ) — COVID-19 Statistics and Analysisii

21. AGE-SPECIFIC AND GENDER-SPECIFIC CASE FATALITY RATE 103 .....................................................................

22. GENDER-SPECIFIC EXPERIENCE — INFECTIONS 106 .............................................................................................

23. GENDER-SPECIFIC EXPERIENCE — DEATHS 110 .....................................................................................................

24. EXCESS MORTALITY 113 ..............................................................................................................................................

25. MORTALITY FORECAST 125 ........................................................................................................................................

26. SOME OVERALL OBSERVATIONS AND CONCLUSIONS 126 .................................................................................

27. OTHER INTERESTING SOURCES 127 .........................................................................................................................

27.1. i.e. Muhanna & co, Actuaries & Consultants 127 ..........................................................................................................................

27.2. Our World in Data 132 ......................................................................................................................................................................

28. COVID-19 — BEYOND THE DATA 133 .........................................................................................................................

28.1. A Personal Account of Navigating the New Normal of a Pandemic Lockdown 133 ................................................................

28.2. Ron Richman — Associate Director, QED Actuaries and Consultants 133 ................................................................................

28.3. Sylvain Goulet — Eckler Ltd. 136 .....................................................................................................................................................

A. LIST OF COUNTRIES AND MAJOR DATA POINTS 139............................................................................................

TOC Page ( ) — COVID-19 Statistics and Analysisiii

L a t e s t S t a t i s t i c s o n t h e C O V I D - 1 9 P a n d e m i c a n d R e l a t e d D i s c u s s i o n s

This document summarizes the statistics on the COVID-19 pandemic. It is updated regularly, often daily, and is circulated to all

interested parties. More charts, tables, text and opinions will be added over time as information becomes available.

1. DISCLOSURE AND RESTRICTIONS OF USE

This document is provided for use by all interested parties, free of charge, provided appropriate credit is given to its authors:

Eckler Ltd (“Eckler”) and QED Actuaries and Consultants (Pty) Ltd (“QED”).

Based on publicly available data, the report has been created based on an EXCEL TOOL developed by Eckler and QED. Anyone

with access to this Excel tool, is bound by the following restrictions:

The information provided in the EXCEL TOOL is strictly confidential to the Party(ies) to which it was directly

communicated (collectively "2nd Party") by Eckler or QED. A 2nd Party may communicate the information to a

3rd Party(ies) (collectively "3rd Party”), provided consent has been obtained from Eckler or QED.

Any Party having access to this information hereby agrees not to disclose it or its content directly or indirectly

to any other party, or to use it for their own interests, without the explicit permission of Eckler or QED.

Continuing to use this EXCEL TOOL implies that the said Party agrees to such restrictions.

The authors have taken care to ensure there are no errors or inaccuracies but are also reliant on other third

parties. Neither Eckler nor QED accept any liability arising out of the use of the Excel model or outputs

therefrom by any other party. By using the Excel tool and output, the parties acknowledge their agreement to

this.

Any questions related to this EXCEL TOOL and restrictions of use can be directed to the individuals listed below. Your full

cooperation in this matter is greatly appreciated. For any query, questions, or clarification, please contact:

Sylvain Goulet — Eckler — [email protected]

Christine Finlay — Eckler — [email protected]

Ronald Richman — QED — [email protected]

Page — COVID-19 Statistics and Analysis2

mailto:[email protected]



2. SCOPE OF THIS REPORT AND INTENDED USE

2.1. Data Source, Reliance, Limitations and Responsibility

The data contained in this report is publicly available through the John Hopkins University Center for Systems Science and

Engineering (https://systems.jhu.edu). The data can also be corroborated against a multitude of other sources. Eckler and QED

take no responsibility for the accuracy of the data. We have simply distilled the data and displayed it visually in various charts.

Our source data currently contains approximately 135,000 datapoints.

2.2. Intended Users of This Report

This report is intended for professionals familiar with statistical analysis such as actuaries, statisticians, and business analysts. It

is expected that such professionals will understand the limitations of statistical analysis such as the credibility of the data as a

function of the number of datapoints observed. For instance, if the number of deaths in one country has increased in 24 hours

from one to two, implying that the rate of death has doubled overnight would be the wrong conclusion.

2.3. Purpose of This Report

The amount of available data online and from other direct sources is vast. Very often, the data is simply delivered in raw format

and not suitable for easy consumption to quickly discern its meaning. The main purpose of this report is to condense the

available data into a format that is easy to understand and from which to make comparisons between countries and regions.

Since the data is only as good as its source and how it was gathered, readers should remain critical as to the reliability of the

data. For instance, if Country A has relatively more confirmed COVID-19 cases than Country B, it does not necessarily imply that

the real rates of infection are higher for Country A. It may be that Country A has done twice as many COVID-19 tests than

Country B, and that consequently the real rates of infection are in fact closer to one another than first thought.

The same caution should be extended to the Case Fatality Rate (CFR). The CFR is defined as the number of deaths due to

COVID-19 to the number of confirmed COVID-19 cases. If Country B under-reports its confirmed COVID-19 cases because it has

conducted fewer COVID-19 tests, the CFR will of course appear to be larger than what it should be had all cases been reported

accurately and in a consistent matter as other countries.

Current data does not give any indication of the extent of under-reporting.


https://systems.jhu.edu

2.4. Assumptions and Methodology, and Rationale

This is not an actuarial report, such as a pricing or valuation report. There are no actuarial assumptions made to derive the

results. The methodology is simple in that the data are simply analyzed and displayed in graphical form. We make no projections

of future rates of infection, crude death rates, or CFRs, unless it is clearly defined. The latter would require a certain and defined

methodology.

The data we have reported in graphical format is very large. To show this amount of data in table format will be very confusing,

hence why we opted to the use of graphs and charts. We have in this report over 180 distinct graphs.

2.5. Use of this Report

This report and the information it contains should not be used in isolation of other findings and research. The COVID-19

pandemic is continuing to evolve and will likely be with us as a Pandemic for a few more months, at which time it may be under

control but still present a threat in one form or another. Until the general population develops some natural immunity to it or the

health services develop a vaccine and an effective cure for the virus, we will need to consider this risk in life and health

insurance.

The findings in this report cannot be used blindly to develop insurance products. They may form the basis to develop insurance

products, but this endeavour must be performed by qualified actuaries. Eckler and QED have qualified actuaries on staff to help

insurance organizations to develop a deep understanding of the findings and use them to design and price insurance products.

In fact, in April 2020, Eckler has already developed such a product for one of its life insurance clients.


3. EXECUTIVE SUMMARY

COVID-19 is the first major pandemic of the 21st century. Unfortunately, it may not be the last. However, so far, it is still less

devastating than the Spanish Flu for instance and the Black Plague. But the environment and the time in which it happens has

an influence too. If COVID-19 had occurred in 1918, maybe it would have been more devastating than the Spanish Flu. But of

course, COVID-19 is not over yet.

One thing that surprises us to some extent is the different experiences between countries, some with high incidence rates like

many countries in Europe such as Belgium and Luxembourg, to very low ones like New Zealand and the English-speaking

Caribbean. This pandemic has developed in waves and continue to do so. It will be with us for sometime to come even after a

vaccine is discovered and deployed. Some people question the reporting from some countries, mostly based on our perception

of these countries. We are not in a position to support or not these claims. We can only accept what the countries are reporting

and analyze the data accordingly.

We saw the first wave in China, in the province of Wuhan. Although there have been many rumours of other origins, this is the

most accepted version. China rapidly brought the outbreak under control by locking down several large cities in the province.

Generally speaking, South-East Asia has done reasonably well at the beginning. But it seems to be in its second wave already

with rapid increases in infection rates in Indonesia and the Philippines, among others. However, countries like Japan and South

Korea have remained low. And although a second wave is also hitting Singapore in terms of infections, the rates of mortality

remain low there.

The pandemic quickly migrated to Europe, and oddly enough seems to have taken it by surprise. It may not have been a

surprise to the residents of Lombardy in Italy, but nonetheless COVID-19 hit Europe like a vengeance and at much higher level

than South-East Asia. Countries like Belgium, Ireland, Italy, Luxembourg, Portugal, Russia, Spain, Sweden and now the United

Kingdom were particularly hit hard. And yet, other European countries like Greece and some Nordic countries are at the very

bottom level. It is very hard to explain the reasons for such large disparities in both infection rates and death rates. For example,

the death rate in Belgium is 843 per million versus 46 per million for Norway. The respective infection rates are 0.53% and 0.16%.

Both countries have first class infrastructure, and yet their experiences have been very different. Both countries have

implemented measures to curb the outbreak. So, how can we explain that?

This takes us to Sweden, a country with a definite different experience, but one of their own doing. Their approach has been

based on developing herd immunity as quickly as possible by doing very little. Their death rate and infection rate are 536 per

million and 0.71%, respectively. The latter number is scary: almost 1% of the population has been infected. In a population of 10

million, 71,000 people have died. And yet, both rates continue to increase rapidly while virtually every other European country

has reached the bottom. One could call this an experiment gone wrong.


Further migrations of the virus take us to the Middle East and Africa. The Middle East has been hit very hard, especially in small

countries like Qatar (less than three million population) with an infection rate of 1.2% and a death rate of 42 per million. Other

Middle East countries are not as high but are doing poorly as well, but at least all seem to have slowed down.

On the other hand, Africa is experiencing some variability by country as well. However bad the experience in countries like

Algeria, Mauritania, Sudan, South Africa and Tanzania are, their levels of infections and death remain very low compared to

Europe. Notwithstanding this note of optimism, South Africa is now on an exponential deterioration with infections increasing

very rapidly.

The next migration takes us to the Americas where the elephant in the room lives. Starting with North America, Canada has

responded quickly and responsibly to the outbreak with severe travel restrictions, social distancing rues, and wearing of masks

among other measures. Restaurants and all public events were closed, and so forth. Canada has even closed its border with the

United States, its largest trading partner. In four months or so, normal life is starting to slowly come back. Mexico has not done

too badly so far, but the jury is still out. The Caribbean experience is also quite diverse. The Spanish-speaking countries like

Dominican Republic and Puerto Rico have not done very well at all. However, Cuba has remained low. The English-speaking

Caribbean, notably the Bahamas, Barbados, Jamaica, and Trinidad and Tobago, have done exceptionally well. Many of these

countries, if not all, have now resumed normal daily life.

That takes us to the elephant in the room, the United States. The US has initially done very poorly in the North-East region of the

country, especially in New York, New Jersey, Connecticut, Massachusetts, and Rhod Island. Treated as countries, they have the

five highest levels of mortality in the world, and four of them are in the top seven highest rates of infection. However, they

eventually brought the outbreak under control, reversing both new infections and new deaths to record low levels. However, the

US has a great appetite for the economy and so after a period of restraints, and not even universal across the country, they re-

opened the country. And now we see a huge surge in both new infections and new deaths at an unprecedented level. States

like California, Florida, Texas, North Carolina, South Carolina, Tennessee, and many others, are out of control. While Florida saw

approximately 3,000 new infections a day, they are now at 9,000. There seems to be no stopping the train.

And the border between Canada and the US remains closed.

We now move to Latin America. The experience there is devastating but also diverse. Countries like Brazil, Chile, Panama, and

Peru, are experiencing levels comparable to some US states. Chile has an infection rate of 1.5% for instance, and a death rate of

317 per million. On the other hand, Argentina and Uruguay are extremely low in comparison. Brazil, with its very large population,

and infection rate of 0.72% and death rate of 291 per million, is now the elephant in the 2nd room.


At the risk of being told that we forgot two main regions, we kept them for the last for two different reasons.

The first one is because of their careful response and leadership in dealing with the outbreak. The countries here are Australia

and most particularly New Zealand where their careful responses led to very low levels of infection and death. Generally

speaking, Oceania has done very well.

The second one is because a giant was sleeping when we were not watching. This is India, which we can call the elephant in

the 3rd room. While India was a sleeping giant throughout most of the initial part of the outbreak, March to May, it has now

awaken. From 2,000 to 4,000 new infections a day, it is now at over 20,000 a day and shows no sign of slowing down. With a

large population of 1.4 billion, and many of them living in tight quarters with dozens of family members in the same household

and other in extreme poverty, this has the potential of being a Tsunami pandemic over the next few months, not just a wave.

So, we now have three elephants: the US, Brazil, and India, with total population of nearly 2 billion. They are all experiencing high levels of

infection and death, and they are all accelerating with no sign of slowing down. Unless these three elephants are brought into control, there is

no guessing at to the potential impact.


4. INTRODUCTION

The COVID-19 pandemic is an event that most of the 7.8 billion people on earth will remember for the rest of their lives. It is

unprecedented in that manner alone. The world of course has seen other pandemics, such as the Black Plague in medieval

times and the Spanish Flu in 1918. There have been more recent pandemic events as well, including SARS, H1N1 and MERS-CoV.

So why is COVID-19 suddenly so scary? We are not epidemiologists and do not claim to make sense of the virus, how it spreads

and so forth. We will leave this to the specialists. However, we are actuaries and we can scrutinize data to make sense of it and

determine some truths and some misconceptions.

To start this discussion, we will make a comparison between various pandemics or pandemic-like situations. When exact

statistics were not available, we used averages. The exact number is not really that important because the scale of the

differences is so large.

The graphs on the next page show the comparison of the infection rates and the crude death rates per million, respectively. The

crude death rate per million is simply the number of deaths divided by the world population at the time, and similarly for the

infection rates.

The data in this section, as well as the subsequent sections until we report specifically on the Americas and other regions, are not necessarily

the latest data available. They were taken at one point in time in order to illustrate the data in a graphical format to explain how to read the

graphs.


Chart 1

Chart 2

28.94

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5.00%

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[A] COVID-19 - INFECTION RATE (19-Jun-2020 - Day 170) - Eckler-1 — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE150,0

00.00

37,83

7.84

689.6

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9

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53.31

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Black P

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[B] COVID-19 - CRUDE DEATH RATE /1,000,000 (19-Jun-2020 - Day 170) - Eckler-1 — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CRUDE DEATH RATE /1,000,000


Again, the data presented in these first few charts is intended for demonstration purposes only and may not always reflect the most current

data available.

The overall infection rate of COVID-19 as of the date shown on the graph (Chart 1) is 0.11% (As of April 13th, it was 0.0246%).

Comparatively, the Swine Flu in 2009-10 had an infection rate of 15.4% or 140 times greater. Of course, the COVID-19 pandemic

is not over yet, and has not reached its peak. Nonetheless, matching the Swine Flu infection may be a tall order, especially given

the extent of government reactions, such as lockdowns, intended to limit the spread of the virus.

The crude death rate per million (Chart 2) compares at 59 per million for COVID-19 (15.33 as of April 13th) versus 53.31 per million

for the Swine Flu, a factor of only 0.90. Compared to the Hong Kong Flu at 281, it is a factor of 4.76. The Avian Flu has a

comparative factor of 690. On the other hand, the final death rates for COVID-19 are still unknown. So, one might wonder, why

does the world needed to shut down due to COVID-19?

4.1. Case Fatality Rate

The culprit is the Case Fatality Rate or CFR. The CFR is basically the death rate “if” you get infected in the first place. The

following graph shows us the comparison:

Chart 3

51.82%

14.00%

0.21%

0.20%

9.56%

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34.40%

5.31%

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10.00%

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[C] COVID-19 - CASE FATALITY RATE (19-Jun-2020 - Day 170) - Eckler-1 — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


If we use the Spanish Flu pandemic as a comparison of something that can be considered dramatic, worldwide, and recent

enough to have reliable date, we can relate the COVID-19 state to the Spanish Flu.

In terms of rates of infection, COVID-19 is currently 0.4% of the Spanish Flu level. In terms of death rate per million, COVID-19 is currently

0.156% of the Spanish Flu level. In terms of case fatality rate, COVID-19 is currently equal to 38% of the Spanish Flu level.

Based on these observations, COVID-19 is only a small portion of the Spanish Flu with respect to rates of infection and rates of death. Without

diminishing the impact of the COVID-19, the rates of infection and rates of deaths are only a fraction of those of the Spanish Flu. However, the

case fatality rate is high in comparison at 38% of that of the Spanish Flu.

On average, across all countries, 7% of reported cases have subsequently died (it was 5% on April 1st). The Swine Flu, the Hong

Kong Flu, and the Avian Flu had a corresponding average death rate of 0.03%, 0.20%, and 0.21%, respectively. The ones who

died may have already been compromised from a health standpoint, but this is true for all ages under all conditions. The CFR for

SARS was large at 9.56%, but the infection rate was actually 0.0001% (1 in a million). MERS-CoV was deadly at 34%, but it was not

a global pandemic and localized in the Middle East, and with an infection rate of 0.00003% (0.3 in a million). In comparison, the

Spanish Flu CFR was 14% and the estimated Black Plague CFR was estimated at above 50%.

Moreover, the COVID-19 CFR shows huge differences between countries and continents. For example, the CFR in Belgium is

almost 16%, 15% in France and the UK, compared to Luxembourg at 2.5% or Germany at about 4%. The difference is hard to

explain and many theories are circulating on the Internet. One recent theory involves the prevalence of vaccinations for other

diseases, or the possibility of different strains of the virus. Other possibilities are different age structures of the population, with

Italy being noted as having a relatively high average age, or the speed with which a country’s public health officials intervened.

The average CFR for Europe is 10%; this is a poor prospect.


Chart 4

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[C] COVID-19 - CASE FATALITY RATE (19-Jun-2020 - Day 170) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


4.2. The Ultimate Risk

The ultimate risk in a pandemic scenario such as COVID-19 is death. Beyond death there is no return. If the infection rate is high

and the CFR is low, then it is mainly an annoyance, like having the common flu every year, or a simple cold. If infected individuals

are expected to be cured in a reasonable period of time, it may still be serious but not fatal in most cases. If the infection is very

low and the CFR very high, like SARS with an infection rate of one in a million and a CFR of 9.56%, it is bad odds for those

infected but is not a worldwide pandemic.

However, if the infection is reasonably high, like COVID-19 at about three times of infecting others than the common flu is, and

the CFR is high at least in countries like Italy, Spain, and France, then this results in rational and reasonable panic. Hence, the

movement by countries to initiate social distancing. Social distancing and containment are implemented to flatten the curve of

infection. This means the total infections will be somewhat longer in emerging but potentially lower in severity, helping to reduce

the strain on the health care systems and hopefully allowing them to cure more people to reduce the CFR.

This is where the following graph comes in. It shows the severity of the infections in function of the number of days it took to get

there.

Chart 5

This graph shows that China was able to flatten the curve at about day 30 after the number of infections reached 10. South

Korea managed it as well around day 40, albeit at a much lower level. However, the rest of Asia continues to increase mainly

due to Singapore, still Japan and now Indonesia the Philippines.

1

10

100

1,000

10,000

100,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

No.

Infe

ctio

ns

No. of Days since No. Infections ≥ 10

[AA] No. of Days since No. Infections ≥ 10 - Asia (19-Jun-2020 - Day 170) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

China (84,494) Korea, South (12,373) Indonesia (43,803) Japan (17,658) Malaysia (8,535) Philippines (28,459) Singapore (41,615) Taiwan* (446) Thailand (3,146) Vietnam (349)


China

Singapore

South Korea

Taiwan

Vietnam

Indonesia

Let us examine the situation in Europe?

Chart 6

After a massive and fast increase in the number of infections, virtually all of Europe has now flattened the curve, except for

Russia, which is still accelerating.

1

10

100

1,000

10,000

100,000

1,000,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

No.

Infe

ctio

ns


[AA] No. of Days since No. Infections ≥ 10 - Europe (19-Jun-2020 - Day 170) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Belgium (60,476) France (196,083) Germany (190,299) Ireland (25,368) Italy (238,159) Netherlands (49,634) Portugal (38,464) Russia (568,292) Spain (245,575) United Kingdom (303,285)


However, in the Americas, the curves are still going up, certainly for the US, Brazil, Peru, and Mexico. There is also a relative big

surge for Venezuela.

Chart 7

The response from the US has been relatively slow when compared to most European countries. The rate of infection is also

increasing exponentially. Because of its large population, the number of infected people has now exceeded 2.2 million. Canada

has not yet reached the same exponential growth. With the exclusion of Brazil, Peru and Mexico, the rest of the countries in the

Americas have not see large levels yet.

4.3. Some of the Other Risks

Some researchers have found that people who have recovered from COVID-19 have lung damage, which could turn out to be

permanent. Others have observed that some patients may have neurological symptoms such as loss of taste or smell.

For life insurers, there may be potential for long-term financial risks related to morbidity risks rather than mortality risks.

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

No

. In

fect

ion

s


[AA] No. of Days since No. Infections ≥ 10 - Americas (19-Jun-2020 - Day 170) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Canada (102,314)

US (2,220,961)

Mexico (170,485)

Barbados (97)

Jamaica (652)

Trinidad and Tobago (123)

Argentina (39,570)

Brazil (1,032,913)

Peru (244,388)

Venezuela (3,591)


5. RELIABILITY OF DATA AND PROJECTIONS

There have been many efforts to project the infections and deaths under COVID-19, generally with very large confidence

intervals around the best-estimate. For instance, on April 3rd, the Ontario government released projections of anywhere from

3,000-5,000 to 80,000-100,000 deaths before the end of the pandemic. As of May 22nd, after the social distancing rules have

been relaxed in Ontario, there have been 2,129 deaths in Ontario. Although the pandemic is not over, the upper end is very

unlikely to occur. This demonstrates the danger of trying to predict the final outcome.

Whereas projections such as these might invoke public health responses, from an actuarial point of view, making projections

that will be more than ±10% to ±20% is highly speculative, and in our view, not yet fit for purposes such as solvency projections

or own risk analyses. In fact, we would rather recommend scenario analysis. Public health experts, and others with

epidemiological modelling skills, do not know exactly how this will pan out at the end. Nor do we. We can take Europe as an

example, where the virus may have peaked and consider the efficacy of mitigation strategies such as lockdowns:

Chart 8

The infection rate goes from a low of 0.031% for Greece to a high of 0.6548% in Luxembourg and around 0.52% for Belgium,

Ireland, and Spain, and new Sweden increasing to 0.555%. There might of course be some under-reporting. But even so, there

is a large difference.

0.1918%

0.5218%

0.0972%

0.2174%

0.1287%

0.3004%

0.2271%

0.0311%

0.5138%

0.3937%

0.6548%

0.2897%

0.1610%

0.3772%

0.3894%

0.5252%

0.5549%

0.3609%

0.4468%

0.3038%

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

[A] COVID-19 - INFECTION RATE (19-Jun-2020 - Day 170) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


Here we examine the “ratios” of the infection rates in Europe shown in the following graph, compared to Europe as a whole (the

y-axis shows this ratio).

Chart 9

Let us assume that we trust the reporting of some countries more than others, in particular the European countries shown

above.

The above numbers, excluding Europe as a whole, have an average ratio of 1.11 for the infection rate and a standard deviation of

0.59. The ratio for the rates of deaths has a similar average of 1.13, but with a much higher standard deviation at 1.00. With this

type of relationship, the data cannot be relied upon to represent Europe as a whole. It is clearly not a matter of the virus alone

but more importantly the countries’ readiness and reaction to the virus. Consequently, at this stage, we reckon that it is

impossible to reliably project the infection rates, let alone the CFR, without taking into account some kind of multiplier or index

based on the country. This multiplier or index has, at this stage, not been identified.

0.63

1.72

0.32

0.72

0.42

0.99

0.75

0.10

1.69

1.30

2.16

0.95

0.53

1.24 1.28

1.73 1.8

31.19

1.47

1.00

0.0

0.5

1.0

1.5

2.0

2.5

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

[G] COVID-19 - Ratio of INFECTION RATE RATES to Europe (19-Jun-2020 - Day 170) -Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


Turning to death rates, some observers have noted that the increase in deaths cannot all be attributed to COVID-19. Instead, in

some cases, more general respiratory ailments may be the cause. Thus, even the death rate figures presented should be

treated with some caution by anyone who wishes to use these figures.

The COVID-19 pandemic is being tracked in a huge amount of detail. We try to make sense of it in the next sections, but before

the data is put to use, some potentially serious shortcomings must be acknowledged.

The following sections contain analyses of the COVID-19 data using clustering analysis and regression modelling. The document then provides

charts for different regions, namely The Americas, The Caribbean, Europe, Asia and Africa.


6. CLUSTERING ANALYSIS

A relatively large amount of data covering infection rates, defat rates and Case Fatality Rates for many countries is now

available. How can we make sense of this mass of data? In this section, we try to provide some answers based on clustering

analysis. Clustering analysis tries to group similar observations into the same groups, and then by understanding the general

characteristics of each group, we can get a better sense of the underlying data, and compare similar (and different) countries.

We present two sets of analysis in this section. The first analysis considers the last 20 days of the pandemic and clusters

together countries with similar rates. The second analysis considers the data since countries first recorded 10 infections, which,

in some countries, was over two months ago or more. For the technical details of how this analysis was produced, please

contact the authors of this report.

6.1. Analysis 1

In Chart 10, we present an analysis of the death rates for the past 20 days. The death rates were “distilled” in a simpler two-

dimensional representation. These points are then clustered into groups which contain nearby observations. Representative

death rates for each cluster are shown in Chart 11 and the mean for each cluster is shown by a dashed line. See the annotations

on the plots for interpretation.


Chart 10


Chart 11


6.2. Analysis 2

In Chart 12, we present an analysis of the death rates for all countries that have registered 10 or more infections. Similar to

Analysis 1, the death rates were “distilled” into a simpler two-dimensional representation. These points are then clustered into

groups which contain nearby observations. Death rates for each country are shown in Chart 13, split by the clusters shown in

Chart 12, with representative countries labelled in this chart. Chart 12 is harder to interpret by itself, but we can gain some insight

from Chart 13. See the caption of Chart 13 for this interpretation.

Chart 12


Chart 13


7. CORRELATION ANALYSIS

7.1. Infection Rates and Case Fatality Rate

Let us consider the correlation between infection rates and CFRs. In other words, if a country reports more infections, does that

imply that more deaths will follow? Although intuitively the answer is yes, the numbers say otherwise. If we examine closely the

infection rate and CFRs datasets, it is likely that reporting of death rates is relatively more accurate than infection rates. For

example, an unintentional under-reporting of infection rates could occur in a country that has performed a large number of

COVID-19 tests, focusing on people with symptoms of COVID-19. Considering that many cases are reported to be asymptomatic

or with only mild symptoms, the persons affected may just stay at home and not report their case. In short, if you do not test, you

cannot report.

On the other hand, if a person dies due to complications of COVID-19, it is more likely that they would have been diagnosed and

in a hospital, or were diagnosed after the fact. Although, even then there are reports that some deaths due to respiratory

ailments similar to COVID-19 have not been reported. In a sense, death rate reporting may be less misleading than infection rate

reporting which probably varies from relatively accurate in some cases to quite inaccurate in others. The following chart

illustrates clearly the lack of correlation, in this case for Europe with a R2 factor of only 0.1189, showing that reported death rates

are not easily predicted by reported infection rates. For instance, compare France to Luxembourg.

Chart 14

Austria

Belgium

Czechia

DenmarkFinland

France

Germany

Greece

Ireland

Italy

Luxembourg

Netherlands

Norway

Portugal

Russia

Spain

Sweden

Switzerland

United Kingdom

Europe

y = 0.0095x + 0.0439R² = 0.1189

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

CASE

FAT

ALIT

Y RA

TE

INFECTION RATE RATE /1,000

[J1] Correlation Analysis between Case Infection Rate and Case Fatality Rate for (19-Jun-2020 - Day 170) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty)

Ltd


7.2. Infection Rates and Testing Rates

The following chart shows a similar relationship, but between infection rates and testing rates. For this illustration, we chose

Europe here as the region to analyze.

Chart 15

This shows a low relationship with a R2 factor of 0.2078. Unfortunately, the testing data is still not as reliable as the infection

rates or death rates. So there are many countries where the data may not be available, or may be even unreliable because it is

not reported frequently. Some countries, like France, are reporting on a weekly basis, so the curves are not as smooth as they

would be in a perfect world.

We can make some other observations, at least with respect to the above chart. One would expect that the Nordic countries

(Denmark, Norway, Sweden and Finland) would have a similar profile. While Sweden has a 6.54 ratio of testing rate over the

infection rate, Denmark has a ratio of 60 and Norway a ratio of 32 demonstrating a low correlation. Finland is at 30.

Austria

Belgium

Czechia

Denmark

Finland

France

Germany

Greece

IrelandItaly

Luxembourg

Netherlands

Norway

PortugalRussia

Spain

Sweden

Switzerland United KingdomEurope

y = 12.187x + 30.325R² = 0.2078

0.00

50.00

100.00

150.00

200.00

250.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

TEST

ING

RATE

/1,0

00


[J2] Correlation Analysis between Case Infection Rate and Testing Rate for (19-Jun-2020 -Day 170) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


8. COUNTRY TO COUNTRY ANALYSIS

We can also analyze the various metrics by comparing a number of countries from the highest level to the lowest (limited to 70

countries for display purposes). In the following charts, the States of the US are considered individually as if they were countries,

as well as the Provinces of Canada. The list is numbered from 1 to 70.

When it comes to the number of infections and deaths, and the rates of infection and death, there is a high level of consistency

across the countries. The top level is made up mostly of the US states and European countries.

However, when we examine the CFR, that is the rate of death for those who are effected, we have a slightly different profile of

the countries experiencing the highest levels. We have eliminated the countries with less than 25 deaths in this analysis.

Here, the top level is made up mostly of Latin Americas and the Caribbean, Africa, and to a smaller extent still Europe. This can

be observed in the 5th chart in this series.

The first 6th chart in the series shows the rates of recovery. This is of course highly influenced by how long the pandemic had

been present in the countries. That explains why many of the top recoveries are for Asian countries.


Chart 16

2,901,1152,801,6782,794,153

2,470,4592,443,304

1,539,081666,941

648,315446,710

395,872295,599288,089285,787

250,545250,514245,251241,184235,429

221,896204,222203,456201,801196,780

185,591178,594177,124172,742

156,391145,750

109,628106,962106,39298,65393,39291,87290,49384,838

72,78672,71172,17571,41970,26268,96164,39363,28962,99761,72760,69560,65756,02055,68255,25750,54650,14148,71248,67247,70546,91546,71745,71944,47943,92943,15641,86541,53240,33637,62437,40736,81835,995

0 250,000 500,000 750,000 1,000,000 1,250,000 1,500,000 1,750,000 2,000,000 2,250,000 2,500,000 2,750,000 3,000,000

1—Northern America2—Latin America and the Caribbean

3—US4—Asia

5—Europe6—Brazil

7—Russia8—India

9—Africa10—US_New York

11—Peru12—Chile

13—United Kingdom14—Spain

15—US_California16—Mexico

17—Italy18—Iran

19—Pakistan20—France21—Turkey

22—Saudi Arabia23—Germany24—US_Texas

25—US_Florida26—South Africa

27—US_New Jersey28—Bangladesh

29—US_Illinois30—US_Massachusetts

31—Canada32—Colombia

33—Qatar34—US_Pennsylvania

35—US_Arizona36—US_Georgia

37—China38—Argentina

39—Egypt40—US_Michigan

41—Sweden42—US_North Carolina

43—US_Maryland44—US_Virginia

45—US_Louisiana46—Belarus

47—Belgium48—Indonesia

49—Ecuador50—Iraq

51—Canada_Quebec52—US_Ohio

53—Netherlands54—United Arab Emirates

55—US_Tennessee56—Kuwait

57—Ukraine58—US_Indiana

59—US_Connecticut60—Kazakhstan

61—Singapore62—Oman

63—Portugal64—US_Alabama

65—US_South Carolina66—Philippines

67—US_Minnesota68—Canada_Ontario

69—Bolivia70—Panama

COVID-19 - No. Infections (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Chart 17

192,953

138,156

129,434

123,107

61,884

59,880

44,216

34,833

32,137

29,896

29,843

28,385

18,655

15,164

11,260

10,870

10,226

9,844

9,765

9,010

8,722

8,149

7,005

6,746

6,315

6,215

6,132

6,051

5,560

5,420

5,186

4,700

4,641

4,551

4,335

3,851

3,684

3,278

3,223

3,201

3,036

2,952

2,903

2,857

2,740

2,681

2,592

2,262

1,968

1,965

1,845

1,802

1,798

1,740

1,708

1,701

1,598

1,507

1,503

1,437

1,419

1,352

1,320

1,280

1,227

1,103

1,047

1,006

977

960

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

1—Europe

2—Northern America

3—US

4—Latin America and the Caribbean

5—Brazil

6—Asia

7—United Kingdom

8—Italy

9—US_New York

10—France

11—Mexico

12—Spain

13—India

14—US_New Jersey

15—Iran

16—Africa

17—Peru

18—Russia

19—Belgium

20—Germany

21—Canada

22—US_Massachusetts

23—US_Illinois

24—US_Pennsylvania

25—US_California

26—US_Michigan

27—Netherlands

28—Chile

29—Canada_Quebec

30—Sweden

31—Turkey

32—Ecuador

33—China

34—Pakistan

35—US_Connecticut

36—Colombia

37—US_Florida

38—US_Louisiana

39—US_Maryland

40—Egypt

41—Indonesia

42—South Africa

43—US_Ohio

44—US_Georgia

45—Canada_Ontario

46—US_Indiana

47—US_Texas

48—Iraq

49—Bangladesh

50—Switzerland

51—US_Virginia

52—Saudi Arabia

53—US_Arizona

54—Ireland

55—Romania

56—US_Colorado

57—Portugal

58—Poland

59—US_Minnesota

60—Argentina

61—US_North Carolina

62—US_Washington

63—Bolivia

64—Philippines

65—Ukraine

66—US_Mississippi

67—US_Missouri

68—US_Alabama

69—Japan

70—US_Rhode Island

COVID-19 - No. Deaths (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Chart 18

34,242

20,363

19,330

16,696

16,088

15,714

15,070

14,479

13,625

13,111

12,451

12,130

11,513

11,397

11,336

10,069

9,930

9,580

9,220

8,965

8,602

8,529

8,441

8,429

8,342

8,120

7,971

7,865

7,727

7,603

7,465

7,444

7,284

7,272

7,241

7,185

7,104

7,072

7,062

6,955

6,667

6,621

6,600

6,599

6,522

6,297

6,273

6,094

5,797

5,747

5,436

5,380

5,359

5,326

5,181

5,164

5,070

4,886

4,794

4,704

4,570

4,460

4,458

4,325

4,284

4,271

4,232

4,210

3,989

3,830

0 2,500 5,000 7,500 10,000 12,500 15,000 17,500 20,000 22,500 25,000 27,500 30,000 32,500 35,000

1—Qatar

2—US_New York

3—US_New Jersey

4—Bahrain

5—US_Rhode Island


7—Chile

8—US_District of Columbia

9—US_Louisiana

10—US_Connecticut

11—US_Arizona

12—US_Delaware

13—US_Illinois

14—Kuwait

15—US_Maryland

16—US_Nebraska

17—US_Mississippi

18—US_Iowa

19—Armenia

20—Peru

21—Oman

22—US_Alabama

23—US

24—US_Georgia

25—Panama

26—US_Florida

27—US_South Carolina


29—US_South Dakota

30—Singapore

31—US_Virginia

32—US_Arkansas


34—US_Utah

35—Brazil

36—US_Michigan

37—Luxembourg

38—Sweden

39—US_Tennessee

40—US_Indiana

41—Belarus

42—US_North Carolina

43—US_Minnesota

44—US_Nevada

45—Canada_Quebec

46—US_Texas

47—US_California

48—US_New Mexico

49—Saudi Arabia

50—US_Colorado

51—Iceland

52—US_Kansas

53—Spain

54—Belgium

55—US_Wisconsin

56—Ireland

57—United Arab Emirates

58—US_North Dakota

59—Djibouti

60—US_Ohio

61—Russia

62—US_Washington

63—Maldives

64—Moldova


66—US_New Hampshire

67—Portugal

68—United Kingdom

69—Italy

70—US_Idaho

COVID-19 - Infection Rate /Million (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Chart 19

1,6971,653

1,2171,168

909843

770706

651651

619607

576553

537530526521

458397

391375369

358352

317310

291291

274266266264258

247244244

231231227227

214205

188186

176173170168168162158158157157152

145142136134134130

1131081071051051019792

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

1—US_New Jersey2—US_New York

3—US_Connecticut4—US_Massachusetts

5—US_Rhode Island6—Belgium

7—US_District of Columbia8—US_Louisiana

9—United Kingdom10—Canada_Quebec

11—US_Michigan12—Spain

13—Italy14—US_Illinois

15—Sweden16—US_Maryland

17—US_Pennsylvania18—US_Delaware

19—France20—US_Indiana

21—US22—Northern America

23—US_Mississippi24—Netherlands

25—Ireland26—Chile27—Peru

28—Brazil29—US_Colorado

30—US_New Hampshire31—Ecuador

32—US_Georgia33—US_Minnesota

34—Europe35—US_Ohio

36—US_New Mexico37—US_Arizona

38—Mexico39—Canada

40—Switzerland41—US_Iowa

42—US_Virginia43—US_Alabama

44—Latin America and the Caribbean45—Canada_Ontario

46—Luxembourg47—US_Washington

48—US_Missouri49—US_Nevada50—US_Florida

51—Panama52—Armenia

53—US_California54—North Macedonia

55—Portugal56—US_South Carolina

57—US_Nebraska58—Moldova

59—US_Wisconsin60—Iran

61—US_North Carolina62—US_Kentucky

63—Bolivia64—Germany

65—US_South Dakota66—US_North Dakota

67—Denmark68—US_Oklahoma

69—US_Kansas70—US_Arkansas

COVID-19 - Death Rate /Million (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Chart 20

27.0%

15.8%

15.5%

14.6%

14.4%

14.1%

12.2%

12.1%

11.3%

10.6%

10.0%

9.3%

8.8%

8.6%

8.5%

8.2%

8.1%

7.9%

7.7%

7.6%

7.4%

7.3%

7.2%

6.8%

6.7%

6.4%

6.3%

6.3%

6.2%

6.1%

6.1%

6.0%

5.9%

5.7%

5.7%

5.5%

5.5%

5.5%

5.5%

5.4%

5.3%

5.3%

5.2%

5.1%

5.1%

5.1%

5.0%

4.8%

4.8%

4.8%

4.8%

4.7%

4.7%

4.6%

4.6%

4.5%

4.5%

4.5%

4.4%

4.4%

4.4%

4.3%

4.3%

4.3%

4.3%

4.2%

4.1%

4.1%

4.1%

4.0%

0.0% 2.5% 5.0% 7.5% 10.0% 12.5% 15.0% 17.5% 20.0% 22.5% 25.0% 27.5% 30.0%

1—Yemen

2—Belgium

3—United Kingdom

4—France

5—Italy

6—Hungary

7—Mexico

8—Netherlands

9—Spain

10—Bahamas

11—Canada_Quebec

12—US_Connecticut

13—US_New Jersey

14—US_Michigan

15—Chad

16—Canada

17—US_New York

18—Europe

19—Ecuador

20—Sweden


22—Canada_Ontario


24—Ireland

25—Slovenia

26—US_New Hampshire

27—Niger

28—Sudan

29—Algeria

30—Switzerland

31—Romania

32—Canada_British Columbia

33—Canada_Nova Scotia

34—US_Indiana

35—US_Rhode Island

36—Greece

37—Angola

38—China

39—Guyana

40—Burkina Faso

41—US_District of Columbia

42—US_Ohio

43—US_Louisiana

44—Mali

45—Japan

46—US_Colorado

47—Indonesia

48—North Macedonia

49—US_Illinois

50—Iran


52—US_Maryland

53—Denmark

54—US

55—Germany

56—Finland

57—US_Vermont

58—US_Missouri

59—Liberia

60—Egypt


62—Bulgaria

63—Lithuania

64—US_Delaware

65—Poland

66—Tunisia

67—Guatemala

68—Tanzania

69—Sierra Leone

70—Iraq

COVID-19 - CFR with ≥10 Deaths (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Chart 21

100.0%

97.7%

97.4%

96.7%

96.4%

94.5%

94.2%

93.3%

92.5%

92.2%

91.7%

91.6%

91.6%

91.2%

91.0%

90.6%

90.3%

90.0%

89.9%

89.8%

89.4%

89.0%

88.9%

88.6%

88.5%

87.9%

87.6%

87.2%

85.2%

84.3%

84.2%

83.9%

83.6%

82.6%

82.1%

81.9%

80.7%

80.5%

80.2%

79.4%

79.2%

78.2%

78.1%

77.4%

77.2%

73.9%

72.1%

71.9%

70.6%

69.7%

69.4%

68.9%

68.4%

66.5%

66.5%

66.0%

66.0%

65.9%

65.7%

65.5%

65.1%

64.0%

63.9%

63.7%

63.5%

63.0%

62.9%

60.8%

60.2%

59.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1—Iceland

2—Malaysia

3—New Zealand

4—Djibouti

5—Thailand

6—China

7—Cuba

8—Estonia

9—Finland

10—Denmark

11—Austria

12—Ireland

13—Germany

14—Norway

15—Switzerland

16—Korea, South

17—Luxembourg

18—Sri Lanka

19—Oceania

20—Qatar

21—Singapore

22—Niger

23—Latvia

24—Australia

25—Tunisia

26—Chile

27—Turkey

28—Japan

29—Slovakia

30—Serbia

31—Lithuania

32—Slovenia

33—Iran

34—Zambia

35—Bahrain

36—Maldives

37—Kuwait

38—Guinea

39—Cameroon

40—Italy

41—Belarus

42—Jordan

43—United Arab Emirates

44—Tajikistan

45—Mexico

46—Ghana

47—Croatia

48—Algeria

49—Lebanon

50—Saudi Arabia

51—Romania

52—Morocco

53—Sierra Leone

54—Uzbekistan

55—Asia

56—Hungary

57—Mali

58—Portugal

59—Canada

60—Russia

61—Senegal

62—Brazil

63—Poland

64—Czechia


66—Israel

67—Peru

68—India

69—Spain

70—Oman

COVID-19 - Recovery Rate with ≥1,000 Infections (03-Jul-2020) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


9. SIGNS OF SLOWING DOWN OR ACCELERATING — INFECTION SPEED

9.1. Introduction

The COVID-19 pandemic may remain with us for sometime. However, three major breakthroughs may happen.

(1) The first one is the creation of a vaccine that will help immunize the population.

(2) The second one is the flattening of the curve, or essentially achieving a stable position with no significant

movement of the rates of infection and/or the rates of death. We can observe these changes in some

countries but not in others. The series of graphs in this section illustrate these characteristics.

(3) The third one is an effective cure for those affected. This could be a regime of drugs, lung exercises, or other

combinations.

The x-axis represents the average number of new infections or deaths. The y-axis represents the total number of infections or

deaths. At the beginning of a pandemic, the number of new infections (x-axis) increases rapidly as the total number of infections

(y-axis) keeps increasing. This means the curve will go from the bottom left of the graph towards the top right side of the graph.

A smoothing mechanism has also been used to make the graph more useable by combining the data over a number of days,

from day one to day five. This approach is an educated guess based on the final results. We have found that, in general, using a

three-day average works best. To illustrate this, we compare four countries in the Americas. The US is so large that it distorts the

comparison, so we have excluded it for this purpose.


These charts show the infections and deaths using single day observations:

Here are the infections and deaths using 5-day average observations:

In Charts 20 and 21, we see some clear evidence of a zigzagging effect. Obviously, people get infected or die at a continuous

speed. Since the data gathering is daily, it follows that there will be more fluctuations from day to day.

Detailed charts on the various continents and specific countries are included later in this report. Next in this section, we highlight

the Infection Speed of some key countries.

Chart 22

0

20,000

40,000

60,000

80,000

100,000

120,000

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

Aver

age

Num

ber o

f Tot

al IN

FECT

IONS

per

Day

(ove

r 1 D

ay)

Average Number of New INFECTIONS per Day (over 1 Day)

[L] Measurement on the speeding of slowing down of INFECTIONS Analysis — (Americas

2) — (from 01-Jan-2020 to 03-May-2020 - Day 123) — © 2020 ECKLER LTD and QED

Actuaries & Consultants (Pty) Ltd

Canada

Mexico

Brazil

Peru

Chart 23

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 100 200 300 400 500 600

Ave

rage

Num

ber

of T

otal

DEA

THS

per

Day

(ove

r 1

Day

)

Average Number of New DEATHS per Day (over 1 Day)

[L] Measurement on the speeding of slowing down of DEATHS Analysis — (Americas 2) —(from 01-Jan-2020 to 03-May-2020 - Day 123) — © 2020 ECKLER LTD and QED Actuaries

& Consultants (Pty) Ltd

Canada

Mexico

Brazil

Peru

Chart 24

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 5

Day

s)

Average Number of New INFECTIONS per Day (over 5 Days)

[L] Measurement on the speeding of slowing down of INFECTIONS Analysis — (Americas

2) — (from 01-Jan-2020 to 03-May-2020 - Day 123) — © 2020 ECKLER LTD and QED


Canada

Mexico

Brazil

Peru

Chart 25

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 50 100 150 200 250 300 350 400 450 500

Ave

rage

Num

ber

of T

otal

DEA

THS

per

Day

(ove

r 5

Day

s)

Average Number of New DEATHS per Day (over 5 Days)

[L] Measurement on the speeding of slowing down of DEATHS Analysis — (Americas 2) —(from 01-Jan-2020 to 03-May-2020 - Day 123) — © 2020 ECKLER LTD and QED Actuaries


Canada

Mexico

Brazil

Peru


9.2. Some Key Countries

In this comparison of four key countries, we illustrate the current differences:

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

0 10,000 20,000 30,000 40,000 50,000 60,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Key Countries 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and

QED Actuaries & Consultants (Pty) Ltd

US

China

United Kingdom

Brazil

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

0 500 1,000 1,500 2,000 2,500 3,000

Ave

rage

Num

ber

of T

otal

DEA

THS

per

Day

(ove

r 3

Day

s)

Average Number of New DEATHS per Day (over 3 Days)

Measurement on the speeding of slowing down of DEATHS Analysis — (Key Countries 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


US

China

United Kingdom

Brazil


We can distinguish the great differences between the US and the other three countries. China has completely turned the

situation around. Their one-party system has helped to lock down the country and force social distancing, which reinforces the

point that controlling a pandemic is doable with cooperation from the population. After slowing down, the US has now started to

increase again, both on the infections side and deaths side. Brazil is the next big one that is emerging.

0

10,000

20,000

30,000

40,000

50,000

60,000

2020-02-01 2020-03-01 2020-04-01 2020-05-01 2020-06-01 2020-07-01

Aver

age

Num

ber o

f New

INFE

CTIO

NS p

er D

ay (o

ver 3

Day

s)

Comparison of New INFECTIONS Analysis — (Key Countries 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

US

China

United Kingdom

Brazil

0

500

1,000

1,500

2,000

2,500

3,000

2020-02-01 2020-03-01 2020-04-01 2020-05-01 2020-06-01 2020-07-01

Aver

age

Num

ber o

f New

DEA

THS

per D

ay (o

ver 3

Day

s)

Comparison of New DEATHS Analysis — (Key Countries 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

US

China

United Kingdom

Brazil


Let us examine all the major countries in the world in terms of number of infections and deaths.

Rest of the World (28%)

US (25%)Brazil (14%)

Russia (6%)

India (6%)

Peru (3%)

United Kingdom (3%)Spain (2%)

Italy (2%)Iran (2%)

France (2%)Turkey (2%)Germany (2%)Canada (1%)China (1%)Belgium (1%)Netherlands (0%)Portugal (0%)Switzerland (0%)Ireland (0%)

COVID-19 - MOST INFECTIONS (11,073,085) — WORLD (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


US (25%)Brazil (14%)

Russia (6%)India (6%)

Peru (3%)United Kingdom (3%)Spain (2%)

Italy (2%)Iran (2%)

France (2%)Turkey (2%)Germany (2%)

Canada (1%)China (1%)

Belgium (1%)Netherlands (0%)

Portugal (0%)Switzerland (0%)Ireland (0%)

US (25%)


Brazil (12%)

United Kingdom (8%)

Italy (7%)

France (6%)

Spain (5%)

India (4%)

Iran (2%)Peru (2%)

Russia (2%)Belgium (2%)Germany (2%)Canada (2%)Netherlands (1%)Turkey (1%)China (1%)Switzerland (0%)Ireland (0%)Portugal (0%)

COVID-19 - MOST DEATHS (525,092) — WORLD (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

US (25%)


Brazil (12%)

United Kingdom (8%)

Italy (7%)

France (6%)

Spain (5%)

India (4%)

Iran (2%)

Peru (2%)

Russia (2%)

Belgium (2%)

Germany (2%)

Canada (2%)

Netherlands (1%)

Turkey (1%)

China (1%)

Switzerland (0%)

Ireland (0%)

Portugal (0%)


10. THE AMERICAS

0.2834%

0.8441%

0.1902%

0.0338%

0.0208%

0.3240%

0.0243%

0.0000%

0.0120%

0.0000%

0.0093%0.1

610%

0.7241%

1.5070%

0.3438%

0.8342%

0.8965%

0.0274%

0.0230%

0.7865%

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE

231.0

9391.0

4231.4

624.36

7.59

71.44

3.38

0.00

0.00

0.00

5.72 31

.80291.1

4 316.5

4266.3

9161.7

7310.1

48.0

6

2.07

374.5

4

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



8.15%

4.63%

12.17%

7.22%

3.64%

2.20%

1.39%

0.00%

0.00%

0.00%

6.15%

1.97%

4.02%

2.10%

7.75%

1.94%

3.46%

2.94%

0.90%

4.76%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - CASE FATALITY RATE (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE

106,962

2,794,153

245,251

97 2,361

35,148

721

0 22 0 130 72

,786

1,539,081

288,089

60,657

35,995 295,599

952 6,5

37

2,901,115

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - No. INFECTIONS (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTIONS


8,722

129,434

29,843

7 86 775

10 0 0 0 8 1,437

61,884

6,051

4,700

698

10,226

28 59

138,156

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - No. DEATHS (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

DEATHS0.36

1.07

0.24

0.04

0.03

0.41

0.03

0.00

0.02

0.00

0.01

0.20

0.92

1.92

0.44

1.06 1.14

0.03

0.03

1.00

0.0

0.5

1.0

1.5

2.0

2.5

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - Ratio of INFECTION RATE RATES to Northern America (03-Jul-2020 - Day 184) -

Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


0.62

1.04

0.62

0.07

0.02

0.19

0.01

0.00

0.00

0.00 0.0

20.08

0.78

0.85

0.71

0.43

0.83

0.02

0.01

1.00

0.0

0.2

0.4

0.6

0.8

1.0

1.2Can

ada US

Mexico

Barbad

osCuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - Ratio of DEATH RATES to Northern America (03-Jul-2020 - Day 184) - Americas

— © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


1.71

0.97

2.56

1.52

0.76

0.46

0.29

0.00

0.00

0.00

1.29

0.41

0.84

0.44

1.63

0.41

0.73

0.62

0.19

1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

cia

Saint

Vincent a

nd th

e…

Trinid

ad and

Tobago

Argentin

aBraz

ilChile

Ecuad

orPan

ama

PeruUrugu

ayVene

zuela

Northern Americ

a

COVID-19 - Ratio of CASE FATALITY RATES to Northern America (03-Jul-2020 - Day 184) -Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


Canada

US

Mexico

Barbados

Cuba

Dominican Republic

Jamaica

MartiniqueSaint LuciaSaint Vincent and the Grenadines

Trinidad and Tobago

Argentina

Brazil

Chile

Ecuador

Panama

PeruUruguay

Venezuela

Northern Americay = 0.0002x + 0.037

R² = 0.0008

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

CASE

FAT

ALIT

Y RA

TE


Correlation Analysis between Case Infection Rate and Case Fatality Rate for (03-Jul-2020 -Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Canada

US

MexicoBarbados

Cuba

Dominican RepublicJamaicaMartiniqueSaint LuciaSaint Vincent and the GrenadinesTrinidad and Tobago

Argentina Brazil

Chile

Ecuador

Panama

Peru

Uruguay

Venezuela

Northern America

y = 4.7982x + 5.3862R² = 0.3455

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

TEST

ING

RATE

/1,0

00


Correlation Analysis between Case Infection Rate and Testing Rate for (03-Jul-2020 - Day 184) - Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

176181

No

. In

fect

ion

s


[AA] No. of Days since No. Infections ≥ 10 - Americas (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Canada (106,962)

US (2,794,153)

Mexico (245,251)

Barbados (97)

Jamaica (721)

Trinidad and Tobago (130)

Argentina (72,786)

Brazil (1,539,081)

Peru (295,599)

Venezuela (6,537)

37.74

331.00

128.93

0.2911.33 10.85 2.96 0.38 0.18 0.11 1.40

45.20

212.56

19.12 17.644.31

32.97

3.47

28.44

368.87

0

50

100

150

200

250

300

350

400

Canad

a USMexi

coBarb

ados

Cuba

Domini

can Repu

blicJam

aicaMart

iniqueSai

nt Lu

ciaSai

nt Vincen

t and

…

Trinid

ad and

Tobago

Argentin

a

Brazil

ChileEcu

ador

Panam

a

PeruUrugu

ayVene

zuela

Northern Americ

a

Millions

[M] Population of Selected Countries for Americas — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

POPULATION


10.1. Infection Speed in the Americas

Here is a sample for some countries in the Americas. After a slowdown, the US has picked up again very quickly after re-

opening many parts of the country, while Brazil is still increasing rapidly.

While Brazil continues to increase, Peru and Chile have not significantly slowed down.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

0 10,000 20,000 30,000 40,000 50,000 60,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Americas 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


Canada

Mexico

US

Brazil

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Americas 2) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


Argentina

Chile

Ecuador

Peru


10.2. Emphasis on the US States

Let us focus on the differences in US States,

The rate of infections in New York State is now above 2%. This is one of the highest in the world, except maybe for Qatar. All

European countries are under 0.75%.

Other states showing an infection rate of 1% and above include Illinois, New Jersey, Arizona, Massachusetts, and Maryland.

0.6273%

0.6297% 0.8

120%

2.0363%

0.7284%

1.1513%

0.4704%

0.8429%

0.6621%

0.7185%

1.9330%

0.7465%

0.4460%

1.2451%

1.5714%

0.7062%

0.6955%

0.3754%

1.1336%

0.8441%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

US_Califo

rniaUS_T

exas

US_Flor

idaUS_N

ew YorkUS_P

ennsyl

vania

US_Illin

oisUS_O

hioUS_G

eorgia

US_North

Carolin

aUS_M

ichiga

nUS_N

ew Jerse

yUS_V

irginia

US_Washingto

nUS_A

rizona

US_Mass

achuset

tsUS_T

enness

eeUS_I

ndian

aUS_M

issouri

US_Mary

land US

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - UNITED STATES — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


The death rates have a high correlation with the infection rates at 0.90.

158.1

287.95 16

7.51

1,653.10

526.1

7553.3

3247.1

1266.1

1133.7

2618.7

11,6

96.85

213.8

8173.4

0243.6

81,1

68.05

91.77

397.4

6169.7

1529.8

3391.0

4

0.0

200.0

400.0

600.0

800.0

1,000.0

1,200.0

1,400.0

1,600.0

1,800.0

US_Califo

rniaUS_T

exas

US_Flor

idaUS_N

ew YorkUS_P

ennsyl

vania

US_Illin

oisUS_O

hioUS_G

eorgia

US_North

Carolin

aUS_M

ichiga

nUS_N

ew Jerse

yUS_V

irginia

US_Washingto

nUS_A

rizona

US_Mass

achuset

tsUS_T

enness

eeUS_I

ndian

aUS_M

issouri

US_Mary

land US

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - UNITED STATES — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



Let use examine the speed of infection.

The State of New York eclipses all other states. However, it has most definitely turned the trend with respect to the number of

infections. The other three states shown have also reversed their trend.

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

0 2,000 4,000 6,000 8,000 10,000 12,000

Ave

ra

ge

Nu

mb

er o

f T

ota

l IN

FE

CT

ION

S p

er D

ay

(o

ve

r 3

Da

ys)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (UNITED STATES 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED


US_New York

US_New Jersey

US_Massachusetts

US_Pennsylvania


The next 12 states show some slowing down at times as well, but also increases for some states.

States like California and Texas continue to increase. Other states, most particularly Florida, have accelerated again and rapidly

after the relaxation of the lockdown. Florida is now seen as the next epicentre of the COVID-19 in the US.

There is a similar situation in the state of Washington, North Carolina, South Carolina and Tennessee.

0

50,000

100,000

150,000

200,000

250,000

300,000

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)




US_California

US_Georgia

US_Texas

US_Florida


0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Ave

ra

ge

Nu

mb

er o

f T

ota

l IN

FE

CT

ION

S p

er D

ay

(o

ve

r 3

Da

ys)




US_Illinois

US_Michigan

US_Maryland

US_Washington

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)




US_North Carolina

US_South Carolina

US_Virginia

US_Tennessee


10.3. States Comparison

The following charts show the distribution of infections and deaths across the states.

New York State and New Jersey, a neighbouring state, have a combined 23% of all infections in the United States and 37% of all

the deaths.

Rest of US (21%)

US_New York (14%)

US_California (9%)

US_Texas (7%)US_Florida (6%)

US_New Jersey (6%)

US_Illinois (5%)

US_Massachusetts (4%)

US_Pennsylvania (3%)

US_Arizona (3%)

US_Georgia (3%)

US_Michigan (3%)US_North Carolina (3%)

US_Maryland (2%)US_Virginia (2%)

US_Ohio (2%)US_Tennessee (2%)US_Indiana (2%)US_Washington (1%)US_Missouri (1%)

COVID-19 - MOST INFECTIONS (2,794,153) — UNITED STATES (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Rest of US (21%)US_New York (14%)US_California (9%)US_Texas (7%)US_Florida (6%)US_New Jersey (6%)US_Illinois (5%)US_Massachusetts (4%)US_Pennsylvania (3%)US_Arizona (3%)US_Georgia (3%)US_Michigan (3%)US_North Carolina (3%)US_Maryland (2%)US_Virginia (2%)US_Ohio (2%)US_Tennessee (2%)US_Indiana (2%)US_Washington (1%)US_Missouri (1%)

US_New York (25%)

Rest of US (17%)

US_New Jersey (12%)US_Massachusetts (6%)

US_Illinois (5%)

US_Pennsylvania (5%)

US_California (5%)

US_Michigan (5%)

US_Florida (3%)US_Maryland (2%)

US_Ohio (2%)US_Georgia (2%)

US_Indiana (2%)US_Texas (2%)US_Virginia (1%)US_Arizona (1%)US_North Carolina (1%)US_Washington (1%)US_Missouri (1%)US_Tennessee (0%)

COVID-19 - MOST DEATHS (129,434) — UNITED STATES (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

US_New York (25%)Rest of US (17%)US_New Jersey (12%)US_Massachusetts (6%)US_Illinois (5%)US_Pennsylvania (5%)US_California (5%)US_Michigan (5%)US_Florida (3%)US_Maryland (2%)US_Ohio (2%)US_Georgia (2%)US_Indiana (2%)US_Texas (2%)US_Virginia (1%)US_Arizona (1%)US_North Carolina (1%)US_Washington (1%)US_Missouri (1%)US_Tennessee (0%)


10.4. Emphasis on the Canadian Provinces

The Canadian Provinces also show some different trends.

The province of Québec is definitely showing a much greater rate of infection than the other provinces, approximately three

times that of Ontario, as well as higher than most of Europe. Alberta is somewhat comparable to Ontario. Of course, these rates

pale in comparison to those in Europe, except for Québec.

Québec and Ontario have significantly slowed down. Canada as a whole has now reversed the trend. While Alberta and Nova

Scotia were increasing fast in early May, they have now reversed their situation as well.

0.2543%

0.6522%

0.0577%

0.1871%

0.0236% 0.0674% 0.1089%

0.0212%

0.0501%

0.0171%

0.0000%

0.0272%

0.0000%

0.3129%

0.2349%

0.3989%

0.5359%

0.4210%

0.8441%

0.2834%

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

0.90%

Canad

a_Ont

ario

Canad

a_Que

becCan

ada_

British

…

Canad

a_Alb

erta

Canad

a_M

anitoba

Canad

a_Sa

skat

chew

an

Canad

a_Nova

Scot

iaCan

ada_

New…

Canad

a_Newfo

undla…

Canad

a_Pr

ince

…

Canad

a_North

wes

t…

Canad

a_Yu

kon

Canad

a_Nunav

utFr

ance

Germ

any

Italy

Spain

United K

ingdo

m USCan

ada

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - CANADA — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


0

20,000

40,000

60,000

80,000

100,000

120,000

0 500 1,000 1,500 2,000 2,500

Aver

age

Num

ber o

f Tot

al IN

FECT

IONS

per

Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (CANADA 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


Canada_Ontario

Canada_Quebec

Canada_Alberta

Canada

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

0 50 100 150 200 250 300 350

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)




Canada_British Columbia

Canada_Alberta

Canada_Manitoba

Canada_Saskatchewan


With respect to the Maritime Provinces, Nova Scotia was the one pushing out further but has now definitely reversed its trend.

0

200

400

600

800

1,000

1,200

0 5 10 15 20 25 30 35 40 45

Aver

age

Num

ber o

f Tot

al IN

FECT

IONS

per

Day

(ove

r 3 D

ays)




Canada_Nova Scotia

Canada_New Brunswick

Canada_Newfoundland And Labrador

Canada_Prince Edward Island


10.5. Provinces Comparison

The following charts show the distribution of infections and deaths across the provinces:

Canada_Quebec (52%)

Canada_Ontario (35%)

Canada_Alberta (8%)

Canada_British Columbia (3%)

Canada_Nova Scotia (1%)

Canada_Saskatchewan (1%)Canada_Manitoba (0%)Canada_Newfoundland

And Labrador (0%)Canada_New Brunswick

(0%)

COVID-19 - MOST INFECTIONS (106,962) — CANADA (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Canada_Quebec (52%)


Canada_Alberta (8%)



Canada_Saskatchewan (1%)

Canada_Manitoba (0%)

Canada_Newfoundland AndLabrador (0%)

Canada_New Brunswick (0%)

Canada_Quebec (64%)



Canada_Alberta (2%)Canada_Nova Scotia (1%)

Canada_Saskatchewan (0%)Canada_Manitoba (0%)Canada_Newfoundland

And Labrador (0%)Canada_New Brunswick

(0%)

COVID-19 - MOST DEATHS (8,722) — CANADA (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Canada_Quebec (64%)



Canada_Alberta (2%)


Canada_Saskatchewan (0%)

Canada_Manitoba (0%)

Canada_Newfoundland AndLabrador (0%)

Canada_New Brunswick (0%)


11. THE CARIBBEAN

0.0694% 0.0

974%

0.0264%

0.0338%

0.0075%

0.0208%

0.0140%

0.3240%

0.0204%

0.0325% 0.0

546%

0.0243%

0.2686%

0.0000%

0.0120%

0.0000%

0.0956%

0.0093%

0.0938%

0.1075%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd…

Surin

ame

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE

30.63

28.10

27.97

24.36

5.03 7.59

6.09

71.44

0.00

17.80

9.65

3.38

53.83

0.00

0.00

0.00

22.16

5.72

57.46

24.53

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd…

Surin

ame

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



4.41%

2.88%

10.58%

7.22%

6.67%

3.64%

4.35%

2.20%

0.00%

5.47%

1.77%

1.39% 2.0

0%

0.00%

0.00%

0.00%

2.32%

6.15%

6.12%

2.28%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%Antig

ua and

Barbuda

ArubaBah

amas

Barbad

osBeli

ze

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - CASE FATALITY RATE (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE

68 104

104

97 302,361

23

35,148

23 256

6,230

721

7,683

0 22 0 561

130

98

45,983

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - No. INFECTIONS (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTIONS


3 3 11 7 2

86

1

775

0 14

110

10

154

0 0 0 13 8 6

1,049

0

200

400

600

800

1,000

1,200Antig

ua and

Barbuda

ArubaBah

amas

Barbad

osBeli

ze

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - No. DEATHS (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

DEATHS0.65

0.91

0.25 0.31

0.07 0.1

90.13

3.01

0.19 0.300.51

0.23

2.50

0.00 0.11

0.00

0.89

0.09

0.87 1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - Ratio of INFECTION RATE RATES to Caribbean (03-Jul-2020 - Day 184) -Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


1.25

1.15

1.14

0.99

0.21 0.3

10.25

2.91

0.00

0.73

0.39

0.14

2.19

0.00

0.00

0.00

0.90

0.23

2.34

1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - Ratio of DEATH RATES to Caribbean (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


1.93

1.26

4.64

3.16

2.92

1.60

1.91

0.97

0.00

2.40

0.77

0.61

0.88

0.00

0.00

0.00

1.02

2.70

2.68

1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Antigua a

nd Barb

udaAruba

Baham

asBarb

ados

Belize

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd th

e…Su

rinam

e

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

COVID-19 - Ratio of CASE FATALITY RATES to Caribbean (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


Antigua and Barbuda

Aruba

Bahamas

BarbadosBelize

Cuba

Curacao

Dominican Republic

Grenada

Guyana

HaitiJamaica

Puerto Rico

Saint Kitts and NevisSaint LuciaSaint Vincent and the Grenadines

Suriname

Trinidad and Tobago Virgin Islands

Caribbean

y = -0.0042x + 0.0375R² = 0.0161

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

CASE

FAT

ALIT

Y RA

TE


Correlation Analysis between Case Infection Rate and Case Fatality Rate for (03-Jul-2020 -Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Antigua and BarbudaArubaBahamasBarbadosBelize

Cuba

Curacao Dominican RepublicGrenadaGuyana HaitiJamaica Puerto RicoSaint Kitts and NevisSaint LuciaSaint Vincent and the Grenadines SurinameTrinidad and Tobago Virgin Islands

Caribbean

y = -0.371x + 1.2377R² = 0.0080.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

TEST

ING

RATE

/1,0

00


Correlation Analysis between Case Infection Rate and Testing Rate for (03-Jul-2020 - Day 184) - Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


1

10

100

1,000

10,000

100,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

176181

No.

Infe

ctio

ns


[AA] No. of Days since No. Infections ≥ 10 - Caribbean (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Bahamas (104) Barbados (97) Belize (30) Curacao (23) Dominican Republic (35,148) Guyana (256) Jamaica (721) Saint Lucia (22) Suriname (561) Trinidad and Tobago (130)

0.10 0.11 0.39 0.29 0.40

11.33

0.16

10.85

0.11 0.79

11.40

2.96 2.86

0.05 0.18 0.11 0.59 1.400.10

42.76

0

5

10

15

20

25

30

35

40

45

Antigua a

nd…

ArubaBah

amas

Barbad

osBeli

ze

CubaCurac

ao

Domini

can Repu

blicGren

ada

Guyana

Haiti

Jamaica

Puerto

Rico

Saint

Kitts an

d Nevis

Saint

Lucia

Saint

Vincent a

nd…

Surin

ame

Trinid

ad and

Tobago

Virgin I

sland

sCari

bbean

Millions

[M] Population of Selected Countries for Caribbean — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

POPULATION


11.1. Emphasis on the Caribbean

The data shows a rapid slowing down of infections in most countries, not all:

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25 30 35

Aver

age

Num

ber o

f Tot

al IN

FECT

IONS

per

Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Caribbean 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


Bahamas

Barbados

Jamaica

Trinidad and Tobago

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 100 200 300 400 500 600 700 800 900 1,000

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Caribbean 2) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries


Cuba

Dominican Republic

Puerto Rico

Virgin Islands


12. EUROPE

0.2004%

0.5326%

0.1150%

0.2250%

0.1307%

0.3129%

0.2349%

0.0334%

0.5164%

0.3989%

0.7104%

0.2950%

0.1646%

0.4232%

0.4570%0.5

359%

0.7072%

0.3709% 0.4

210%

0.3268%

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE

78.28

842.5

632.96

104.6

259.38

458.0

1107.5

418.42

352.3

8576.1

2175.7

3357.8

746.30

156.7

267.45

607.1

0536.6

7227.0

5651.3

3258.0

8

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

900.0

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



3.91%

15.82%

2.87%

4.65%

4.54%

14.64%

4.58%

5.51%

6.82%

14.44%

2.47%

12.13%

2.81% 3.7

0%

1.48%

11.33%

7.59%

6.12%

15.47%

7.90%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - CASE FATALITY RATE (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE

18,05061,727

12,31913,032

7,242

204,222

196,780

3,486

25,498 24

1,184

4,447 50

,546

8,921 43

,156

666,941

250,545

71,419

32,101 285,787

2,443,304

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - No. INFECTIONS (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTIONS


705 9,7

65

353

606

329

29,896

9,010

192 1,7

40

34,833

110 6,1

32

251 1,5

98 9,844

28,385

5,420

1,965

44,216

192,953

0

50,000

100,000

150,000

200,000

250,000

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - No. DEATHS (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

DEATHS0.61

1.63

0.35

0.69

0.40

0.96

0.72

0.10

1.58

1.22

2.17

0.90

0.50

1.30 1.4

01.64

2.16

1.131.29

1.00

0.0

0.5

1.0

1.5

2.0

2.5

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - Ratio of INFECTION RATE RATES to Europe (03-Jul-2020 - Day 184) - Europe —© 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


0.30

3.26

0.13

0.41

0.23

1.77

0.42

0.07

1.37

2.23

0.68

1.39

0.18

0.61

0.26

2.35

2.08

0.88

2.52

1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5Austr

iaBelg

iumCzechi

aDen

markFin

land

France

Germany

Greece

Irelan

d

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - Ratio of DEATH RATES to Europe (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


0.49

2.00

0.36

0.59

0.58

1.85

0.58 0.7

00.86

1.83

0.31

1.54

0.36 0.4

7

0.19

1.43

0.96

0.78

1.96

1.00

0.0

0.5

1.0

1.5

2.0

2.5

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

COVID-19 - Ratio of CASE FATALITY RATES to Europe (03-Jul-2020 - Day 184) - Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


1

10

100

1,000

10,000

100,000

1,000,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

176181

No.

Infe

ctio

ns


[AA] No. of Days since No. Infections ≥ 10 - Europe (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Belgium (61,727) France (204,222) Germany (196,780) Ireland (25,498) Italy (241,184) Netherlands (50,546) Portugal (43,156) Russia (666,941) Spain (250,545) United Kingdom (285,787)

9.01 11.59 10.71 5.79 5.54

65.2783.78

10.42 4.94

60.46

0.6317.13 5.42 10.20

145.93

46.7510.10 8.65

67.89

747.64

0

100

200

300

400

500

600

700

800

Austria

Belgium

Czechia

Denmark

Finlan

dFra

nceGerm

anyGree

ceIre

land

Italy

Luxembourg

Netherla

ndsNorw

ayPort

ugal

Russia

Spain

Swed

enSw

itzerla

ndUnite

d Kingdo

mEu

rope

Millions

[M] Population of Selected Countries for Europe — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

POPULATION


12.1. Emphasis on Europe — 1

Here is a comparison of four major European countries among the highest ones affected, using a 3-day average.

While France, Italy and Spain are three of the most affected countries in the world, they have now reversed their trend. In the

United Kingdom, where actions such as social distancing and lockdown were implemented much later, we observe that the

trend has finally slowed down and has now just reversed.

0

50,000

100,000

150,000

200,000

250,000

300,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Europe 1) —(from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries &

Consultants (Pty) Ltd

France

Italy

Spain

United Kingdom



This comparison looks at four other major European countries:

Germany, Portugal and the Netherlands have definitely reversed the trend with respect to infections, even though the number of

infections is quite large. However, Russia is now running away with high infection rates only minimal sign of slowing down,

averaging about 7th new daily infections for the last week.

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

0 2,000 4,000 6,000 8,000 10,000 12,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)




Germany

Netherlands

Portugal

Russia



Finally, the following comparison looks at the four Nordic countries:

The trend is very clearly reversed for Norway, Denmark, and Finland. For Sweden which has resisted lockdown and social

distancing measures, there is a marked indication that their approach has not worked very well. Their infection rates were

stagnant for many weeks ate approximately 550 a day, and has now more than doubled in the last few weeks.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)




Denmark

Finland

Norway

Sweden


13. ASIA (INCLUDING THE MIDDLE EAST and RUSSIA)

0.0059%

0.0254%

0.0222%

0.0152%

0.0267%

0.0368%

0.7603%

0.0019%

0.0046%

0.0004%

0.0950%

0.0470%

0.1005%0.4

570%

1.6696%

0.2803%

0.3241%

3.4242%

0.5797%

0.0532%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE

3.22 5.52 11.10

7.72

3.74 11

.68

4.44

0.29

0.83

0.00

11.95 13.52 20

.6067.45

55.83

134.0

637.66 42.0051.76

12.90

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



5.47%

2.17%

5.00% 5.0

9%

1.40%

3.17%

0.06%

1.56% 1.8

2%

0.00%

1.26%

2.88%

2.05%

1.48%

0.33%

4.78%

1.16%

0.12%

0.89%

2.42%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - CASE FATALITY RATE (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE

84,838

13,03060,695

19,185

8,648 40

,33644,479

449 3,1

80355156,391

648,315

221,896

666,941

28,410 23

5,429

28,05598,653 20

1,801

2,470,459

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - No. INFECTIONS (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTIONS


4,641

283 3,0

36

977

121 1,2

80

26 7 58 01,968

18,655

4,551

9,844

95

11,260

326

121 1,8

02

59,880

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - No. DEATHS (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

DEATHS0.11

0.48

0.42

0.28

0.50

0.69

14.28

0.04

0.09

0.01 1.78

0.88 1.89

8.59

31.37

5.27 6.09

64.33

10.89

1.00

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - Ratio of INFECTION RATE RATES to Asia (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


0.25 0.4

3 0.86

0.60

0.29 0.9

1

0.34

0.02

0.06

0.00

0.93 1.0

5 1.60

5.23

4.33

10.39

2.92 3.2

64.01

1.00

0.0

2.0

4.0

6.0

8.0

10.0

12.0China

Korea,

South

Indonesia

Japan

Malaysi

aPhil

ippines

Singap

oreTa

iwan*Th

ailan

dVietna

mBan

glade

sh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - Ratio of DEATH RATES to Asia (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


2.26

0.90

2.06 2.1

0

0.58

1.31

0.02

0.64 0.7

5

0.00

0.52

1.19

0.85

0.61

0.14

1.97

0.48

0.05

0.37

1.00

0.0

0.5

1.0

1.5

2.0

2.5

ChinaKore

a, So

uthIndon

esiaJap

anMala

ysia

Philipp

inesSin

gapore

Taiwan*

Thail

and

Vietnam

Bangla

desh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

COVID-19 - Ratio of CASE FATALITY RATES to Asia (03-Jul-2020 - Day 184) - Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


Although the curves are flattening in virtually all countries in South-East Asia, Singapore (dark green line) is still accelerating, and

Indonesia (light blue) is also accelerating, as is the Philippines (brown gold). As we saw in the first graph, Singapore rate of

infection is 0.76% while the whole of Asia is approximately 0.053%, a ratio of 14 to 1.

1

10

100

1,000

10,000

100,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

176181

No.

Infe

ctio

ns


[AA] No. of Days since No. Infections ≥ 10 - Asia (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

China (84,838) Korea, South (13,030) Indonesia (60,695) Japan (19,185) Malaysia (8,648) Philippines (40,336) Singapore (44,479) Taiwan* (449) Thailand (3,180) Vietnam (355)

1,439.32

51.27273.52

126.48 32.37 109.58 5.85 23.82 69.80 97.34 164.69

1,380.00

220.89145.931.70 83.99 8.66 2.88 34.81

4,641.05

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

ChinaKore

a, So

uthIndon

esia

Japan

Malaysi

aPhil

ippines

Singap

oreTa

iwan*Th

ailan

dVietna

mBan

glade

sh

IndiaPaki

stan

Russia

Bahrai

n

Iran

Israe

lQata

rSau

di Arabia Asia

Millions

[M] Population of Selected Countries for Asia — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

POPULATION


13.1. Emphasis on Asia

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

550,000

600,000

650,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Asia 1) —(from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries &


China

India

Indonesia

Qatar

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 200 400 600 800 1,000 1,200 1,400 1,600

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Asia 2) —(from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries &


Japan

Philippines

Korea, South

Indonesia


The graphs clearly show how China has made a big reversal of the new infections. Qatar is continuing to increase rapidly, as

well as Singapore, although the latter is slowing down. This is worrying because of their small population, 2.9 million and 5.9

million, respectively.

Indonesia and the Philippines are now increasing rapidly, with total population of 274 million and 110 million. Not containing the

outbreak could result in a significant level of the population being affected.

However, the large uncertainty is now India. India’s infection rate is currently only 0.05% and small in comparison to Singapore

at 0.76%. However, because of its large population of 1.4 billion, the numbers of individuals affected can easily surpass all other

countries.


14. AFRICA

0.0344%

0.0047%

0.0474%0.0

811%

0.0082%0.0

388%0.0

711%

0.0624%

0.0134%

0.1012%

0.0268%

0.0360%

0.0138%

0.0134%

0.0083%0.0

428%

0.2986%

0.0220%

0.0009%0.0

333%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

Algeria

Burkina F

aso

Camero

onCen

tral A

frica

n…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

naKen

yaM

aurit

ania

Mau

ritius

Mor

occo

Namibia

Nigeria

Rwanda

Sene

gal

South

Africa

Sudan

Tanz

ania

Africa

COVID-19 - INFECTION RATE (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE

21.37

2.54

11.79

9.94

2.00

2.65

31.28

3.77

2.86

27.74

7.86

6.23

0.00 3.0

5

0.23

7.47

49.77

13.77

0.35

8.11

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Algeria

Burkina F

aso

Camero

onCen

tral A

frica

n…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

naKen

yaM

aurit

ania

Mau

ritius

Mor

occo

Namibia

Nigeria

Rwanda

Sene

gal

South

Africa

Sudan

Tanz

ania

Africa

COVID-19 - CRUDE DEATH RATE /1,000,000 (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



6.22%

5.41%

2.49%

1.23%

2.45%

0.68%

4.40%

0.60%

2.14%

2.74% 2.9

3%

1.73%

0.00%

2.28%

0.28%

1.74%

1.67%

6.25%

4.13%

2.43%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

Alger

iaBurk

ina F

aso

Camero

on

Centra

l Afri

can R

epub

lic

Congo (

Kinshas

a)Cote

d'Iv

oire

Egyp

tGha

na

Kenya

Mau

ritan

iaM

aurit

ius

Mor

occo

Namib

iaNige

riaRwand

aSe

nega

lSo

uth A

frica

Sudan

Tanz

ania

Africa

COVID-19 - CASE FATALITY RATE (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE

15,070

980 12

,592

3,918

7,311

10,244

72,711

19,388

7,188

4,705

341 13

,288

350

27,564

1,081 7,1

64

177,124

9,663

509

446,710

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

Algeria

Burkina F

aso

Camero

onCen

tral A

frica

n…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

naKen

yaM

aurit

ania

Mau

ritius

Mor

occo

Namibia

Nigeria

Rwanda

Sene

gal

South

Africa

Sudan

Tanz

ania

Africa

COVID-19 - No. INFECTIONS (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTIONS


937

53 313

48 179

70

3,201

117 15

4129

10 230

0628

3 125

2,952

604

21

10,870

0

2,000

4,000

6,000

8,000

10,000

12,000

Algeria

Burkina F

aso

Camero

onCen

tral A

frica

n…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

naKen

yaM

aurit

ania

Mau

ritius

Mor

occo

Namibia

Nigeria

Rwanda

Sene

gal

South

Africa

Sudan

Tanz

ania

Africa

COVID-19 - No. DEATHS (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

DEATHS1.03

0.14

1.42

2.43

0.24

1.17

2.13

1.87

0.40

3.04

0.80 1.08

0.41

0.40

0.25

1.28

8.96

0.66

0.03

1.00

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

Alger

iaBurk

ina F

aso

Camero

on

Centra

l Afri

can R

epub

lic

Congo (

Kinshas

a)Cote

d'Iv

oire

Egyp

tGha

na

Kenya

Mau

ritan

iaM

aurit

ius

Mor

occo

Namib

iaNige

riaRwand

aSe

nega

lSo

uth A

frica

Sudan

Tanz

ania

Africa

COVID-19 - Ratio of INFECTION RATE RATES to Africa (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

INFECTION RATE


2.63

0.31

1.45

1.23

0.25 0.3

3

3.86

0.46

0.35

3.42

0.97

0.77

0.00 0.3

8

0.03

0.92

6.14

1.70

0.04

1.00

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0Algeri

aBurki

na Fas

oCam

eroon

Centra

l Afri

can…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

naKen

yaM

aurit

ania

Mau

ritius

Mor

occo

Namibia

Nigeria

Rwanda

Sene

gal

South

Africa

Sudan

Tanz

ania

Africa

COVID-19 - Ratio of DEATH RATES to Africa (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


2.56

2.22

1.02

0.50

1.01

0.28

1.81

0.25

0.88

1.13 1.2

1

0.71

0.00

0.94

0.11

0.72

0.68

2.57

1.70

1.00

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Alger

iaBurk

ina F

aso

Camero

on

Centra

l Afri

can R

epub

lic

Congo (

Kinshas

a)Cote

d'Iv

oire

Egyp

tGha

na

Kenya

Mau

ritan

iaM

aurit

ius

Mor

occo

Namib

iaNige

riaRwand

aSe

nega

lSo

uth A

frica

Sudan

Tanz

ania

Africa

COVID-19 - Ratio of CASE FATALITY RATES to Africa (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

CASE FATALITY RATE


Algeria

Burkina Faso

Cameroon

Central African Republic

Congo (Kinshasa)

Cote d'Ivoire

Egypt

Ghana

Kenya

MauritaniaMauritius

Morocco

Namibia

Nigeria

Rwanda

Senegal South Africa

Sudan

Tanzania

Africa

y = -0.0044x + 0.028R² = 0.0244

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

CASE

FAT

ALIT

Y RA

TE


Correlation Analysis between Case Infection Rate and Case Fatality Rate for (03-Jul-2020 -Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

AlgeriaBurkina Faso Cameroon Central African RepublicCongo (Kinshasa)Cote d'Ivoire Egypt

Ghana

Kenya

MauritaniaMauritius

Morocco

NamibiaNigeria

Rwanda

Senegal

South Africa

SudanTanzania

Africa

y = 8.1057x + 0.1268R² = 0.4637

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

TEST

ING

RATE

/1,0

00


Correlation Analysis between Case Infection Rate and Testing Rate for (03-Jul-2020 - Day 184) - Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


Although the rates of infection are low so far in Africa, the curves are continuing to increase and show little signs of slowing

down. There is a serious risk of runaway infections as the health systems may not be able to cope with a crisis of a large

magnitude. South Africa (yellow line) is leading Africa in terms of number of infections, followed closely by Egypt (light blue line).

1

10

100

1,000

10,000

100,000

1,000,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96101

106111

116121

126131

136141

146151

156161

166171

176181

No.

Infe

ctio

ns


[AA] No. of Days since No. Infections ≥ 10 - Africa (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

Algeria (15,070) Cote d'Ivoire (10,244) Egypt (72,711) Ghana (19,388) Kenya (7,188) Morocco (13,288) Rwanda (1,081) Senegal (7,164) South Africa (177,124) Tanzania (509)

43.85 20.90 26.55 4.8389.56

26.38102.33

31.07 53.774.65 1.27

36.91 2.54

206.14

12.95 16.7459.31 43.85 59.73

1,340.11

0

200

400

600

800

1,000

1,200

1,400

1,600

Algeria

Burkina F

aso

Camero

onCen

tral A

frica

n…

Congo (

Kinshas

a)Cote

d'Ivoire

Egyp

tGha

na

Kenya

Mau

ritan

iaM

aurit

iusM

oroc

coNam

ibiaNige

riaRwand

aSe

nega

lSo

uth Afri

ca

Sudan

Tanz

ania

Africa

Millions

[M] Population of Selected Countries for Africa — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

POPULATION


14.1. Emphasis on Africa

Africa has now developing their COVID-19 trends. Of particular concerns are Egypt, Nigeria, Cameroon and South Africa, with

the latter accelerating quite rapidly and a combined population of almost 400 million.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000

Ave

rage

Num

ber

of T

otal

INFE

CTIO

NS

per

Day

(ove

r 3

Day

s)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Africa 1) —(from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries &


Cameroon

Egypt

Nigeria

South Africa

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

0 100 200 300 400 500 600

Aver

age

Num

ber o

f Tot

al IN

FECT

ION

S pe

r Day

(ove

r 3 D

ays)


Measurement on the speeding of slowing down of INFECTIONS Analysis — (Africa 2) —(from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries &


Algeria

Ghana

Morocco

Sudan


15. OCEANIA

The results for Oceania are scarce except for Australia, Papua New Guinea, and New Zealand. It is not too much meaningful to

display the results in a graph. However, for comparison purposes, here is how these three countries compared to the rest of the

world.

Obviously, these three countries have done an excellent job at controlling the outbreak.


16. GLOBAL COMPARISON OF RATES OF INFECTION

16.1. A Quick Overview of the World

Each of the previous graphs usually show major countries within their continent. The y-axis is automatically adjusted to fit the

higher limits. So, by design, there is always a country that will reach the upper end of the graph. This is not misleading for its

intended purpose. However, it hides the fact that one country at the upper end of one continent could actually be at the bottom

end of another continent. We continue to look at ways to depict these disparities across all countries. For now, we provide the

following graph:

The above graph shows the relative size of the rates of infection across key countries. For instance, although the US has a rate

of infection compared to, say, Canada, at three to one, it compares to most countries in Europe on average. Moreover, from this

graph we can discern that Europe is dealing with a major situation, albeit almost over now, followed by the US individually. The

middle East is particularly hit hard especially Qatar. As far as S-E Asia (except for Singapore), the Caribbean, Africa, and Oceania

are concerned, their levels of infections and deaths are still relatively low. So, even though the South Africa infections are

accelerating, it is at the moment low by comparison.

0.8441%

0.2834%

0.1902%

0.0243%

0.5326%

0.3129%

0.3989% 0.7

072%

0.4210%

3.4242%

0.3241%

0.0470%

0.0059%

0.7603%

0.0711%

0.0624%

0.0134% 0.2

986%

0.0324%

0.0317%

0.0000%

0.5000%

1.0000%

1.5000%

2.0000%

2.5000%

3.0000%

3.5000%

4.0000%

USCan

ada

Mexico

Jamaica

Belgium

France Ita

lySw

eden

United King

dom

Qatar

Israe

lIndiaChina

Singap

oreEg

yptGha

naNige

riaSo

uth Afric

aAustr

aliaNew

Zealan

d

COVID-19 - INFECTION RATE RATES (03-Jul-2020 - Day 184) - GLOBAL — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


North AmericaEurope

Asia

Middle

East

Africa

SE Asia

Oceania

16.2. The Emerging Giants

Some countries have been affected much more than others. It clearly demonstrates that how a country’s Government has

reacted to the pandemic has impacted the current conditions. This is especially true when we would otherwise expect countries

to have a very similar experience. For instance, we have previously compared Sweden to Denmark, Norway and Finland. We can

compare the US to Canada, maybe excluding the province of Québec. Such a list of similar countries and yet different results is

very long. What we conclude is that the impact of a pandemic can be curtailed to a large extent as long as governments

implement the right measures.

However impactful COVID-19 is to some countries, we have to examine the emerging giants, those with large population, as

measured by new infections. The following countries lead the world’s new infections by number; the US in North America, Brazil

in South America, India in Asia, and Russia in Europe:

Russia has slowed down a little, but the other three giants are increasing rapidly. The US and Brazil are in a tug of war, while

India can be the sleeping giant. In a country of nearly 1.4 billion people where even “normal” deaths go unreported, it has the

potential to reach a catastrophic level.

0

10,000

20,000

30,000

40,000

50,000

60,000

2020-02-01 2020-03-01 2020-04-01 2020-05-01 2020-06-01 2020-07-01

Ave

rage

Num

ber

of N

ew IN

FECT

ION

S pe

r D

ay (o

ver

3 D

ays)

Comparison of New INFECTIONS Analysis — (Key Countries 3) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

US Brazil

India Russia


17. TIMELINE OF COVID-19 BY CONTINENT

Another type of graphs that can be very useful in comparing the emerging experience over a timeline and across continents is a

stacked bar graph.

The following graphs show the total infections, the new infections, the total deaths and the new days, on a daily basis since 1st

January 2020 to the most recent data available.

When examining the new infections graph, we can see that the pace of increase ha significantly slowed down from it it peaked

in early April 2020, that is for all regions except for Latin America and the Caribbean, and to some extent Asia. It is clear that

Latin America and the Caribbean (mainly the Spanish-speaking Caribbean) is on a fast increasing scale when it comes to new

infections.

The new deaths graph tells a different story. From the peak in mid-April, the new deaths have started to decline. There seems to

be some anomalies in the data, but overall it is also quite clear that the total deaths are decreasing.


0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 1) - INFECTIONS (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

AFRICA ASIA EUROPE NORTH AMERICA LATIN AMERICA & CARIBBEAN OCEANIA

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 7) - NEW INFECTIONS (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



0

100,000

200,000

300,000

400,000

500,000

600,000

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 1) - DEATHS (03-Jul-2020 -

Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 7) - NEW DEATHS (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



The following graphs more clearly show the growing proportion of infections and deaths attributed to Latin America, currently

35% for new infections and 38% for new deaths:

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 7) - NEW INFECTIONS (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2020-0

2-01

2020-0

2-08

2020-0

2-15

2020-0

2-22

2020-0

2-29

2020-0

3-07

2020-0

3-14

2020-0

3-21

2020-0

3-28

2020-0

4-04

2020-0

4-11

2020-0

4-18

2020-0

4-25

2020-0

5-02

2020-0

5-09

2020-0

5-16

2020-0

5-23

2020-0

5-30

2020-0

6-06

2020-0

6-13

2020-0

6-20

2020-0

6-27

COVID-19 - TIMELINE OF COVID-19 BY CONTINENT (AVG #DAYS: 7) - NEW DEATHS (03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



18. AGE-SPECIFIC AND GENDER-SPECIFIC CASES

The data on age-specific experience is not entirely available and what is provided at this stage must be used with caution until

we have more reliable data. Nonetheless, it shows interesting differences between ages. The data is also partly inconsistent by

country, so what we are reporting here should be treated carefully.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Belgium Germany Italy Netherlands Spain — Distribution of Infections (758,587) by Age Group and Gender

MALE FEMALE UNKNOWN

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Portugal Austria Sweden Switzerland — Distribution of Infections (71,981) by Age Group and Gender

MALE FEMALE UNKNOWN


The pattern of cases varies by country. This of course may depend on many factors, including the actual age distribution. It

usually increases by age and then eventually decreases as the total number must decrease since the exposure is much lower at

the older ages. However, Asia shows a strange pattern of increase cases at the 20-30 age bracket.

0

100

200

300

400

500

600

700

800

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Norway Estonia Greece Ireland — Distribution of Infections (13,796) by Age Group and Gender

MALE FEMALE UNKNOWN

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

China Japan South Korea — Distribution of Infections (28,714) by Age Group and Gender

MALE FEMALE UNKNOWN


0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Canada USA Mexico — Distribution of Infections (130,948) by Age Group and Gender

MALE FEMALE UNKNOWN

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Colombia Ecuador — Distribution of Infections (80,405) by Age Group and Gender

MALE FEMALE UNKNOWN


0

10

20

30

40

50

60

70

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Algeria Eswatini — Distribution of Infections (728) by Age Group and Gender

MALE FEMALE UNKNOWN


19. AGE-SPECIFIC AND GENDER-SPECIFIC “RATES” OF INFECTION

The rates of infection is clearly a function of age, with older ages experiencing significantly higher rates.

0.0%

0.0%

0.0% 0.1% 0.1% 0.2% 0.3%

0.3%

0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 0.5% 0.6%

0.8%

1.2%

1.5%

2.4%

2.9%

0.03%

0.04%

0.04%

0.09% 0.22% 0.40%

0.41%

0.37%

0.38%

0.43%

0.44%

0.39%

0.32%

0.29%

0.33%

0.45% 0.

71%

1.25%

1.83%

2.71%

3.99%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Belgium Germany Italy Netherlands Spain — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN

0.1%

0.1%

0.1% 0.2% 0.3% 0.5%

0.4%

0.4%

0.4% 0.4%

0.4%

0.4%

0.4%

0.4%

0.4% 0.4% 0.5% 0.8%

1.3%

2.5%

12.0%

0.07%

0.07%

0.10%

0.20%

0.43%

0.61%

0.52%

0.43%

0.45%

0.49%

0.52%

0.46%

0.36%

0.29%

0.28%

0.33%

0.48%

0.80% 1.41%

2.62%

7.72%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Portugal Austria Sweden Switzerland — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN


0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1%

0.1%

0.1% 0.1% 0.1%

0.1%

0.1%

0.1%

0.1%

0.1% 0.1% 0.1% 0.

1%

0.2%

1.3%

0.01%

0.02%

0.03%

0.05% 0.10%

0.12%

0.11%

0.09%

0.10%

0.11%

0.10%

0.10%

0.07%

0.06%

0.05%

0.06%

0.07%

0.09%

0.13% 0.18%

0.60%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Norway Estonia Greece Ireland — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN

0.0%

0.0% 0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0% 0.

0% 0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.00%

0.00%

0.01%

0.03%

0.04%

0.02%

0.01%

0.01%

0.02%

0.02%

0.02%

0.02%

0.01%

0.01% 0.01% 0.01% 0.

02%

0.02%

0.03%

0.04%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.01%

0.01%

0.02%

0.02%

0.03%

0.03%

0.04%

0.04%

0.05%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

China Japan South Korea — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN


0.0%

0.0% 0.0% 0.0%

0.0%

0.0%

0.0%

0.0% 0.

0% 0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.1%

0.1%

0.1%

0.1%

0.1%

0.00%

0.00%

0.00% 0.01%

0.02%

0.03% 0.03%

0.03% 0.04% 0.04%

0.04%

0.04%

0.03%

0.02%

0.02% 0.03%

0.05%

0.08%

0.09% 0.10%

0.12%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

0.12%

0.14%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Canada USA Mexico — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN

0.0%

0.0% 0.0%

0.1%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2%

0.2% 0.2%

0.2%

0.2%

0.2% 0.2%

0.2%

0.3%

0.3%

0.2%

0.4%

0.03%

0.03%

0.04% 0.05%

0.10%

0.14%

0.14%

0.14%

0.12%

0.12%

0.11%

0.11%

0.10%

0.09%

0.10%

0.11% 0.

13% 0.14%

0.15% 0.16%

0.05%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Colombia Ecuador — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN


0.0%

0.0%

0.0% 0.

0%

0.0%

0.1%

0.2%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1%

0.1% 0.1% 0.1%

0.1%

0.1%

0.2%

0.0%

0.0%

0.02%

0.02%

0.02% 0.03%

0.06%

0.10% 0.

11%

0.11%

0.09%

0.10%

0.17%

0.15%

0.04%

0.03% 0.04%

0.03%

0.02%

0.02% 0.03%

0.00%

0.00%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Algeria Eswatini — Rate of Infection by Age Group and Gender

MALE FEMALE UNKNOWN


20. AGE-SPECIFIC AND GENDER-SPECIFIC “RATES” OF DEATH

The rates of death is not dissimilar.

0 0 0 1 2 4 7 13 24 45 84 162 319 624 1,

201 1,999

3,155

5,300

7,292

11,659

14,307

1 1 0 1 1 3 4 7 12 20 32 56 108

211 438 829

1,563

3,099

4,983

8,044

12,398

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Belgium Germany Italy Spain — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN

1 1 0 0 2 4 8 14 22 40 82 160

283

479 773 1,334 2,382 3,914

6,213

10,889

27,206

0 0 0 1 1 2 5 9 12 20 39 68 109

173

282

530 1,058 1,907 3,137

5,326

9,811

0

5,000

10,000

15,000

20,000

25,000

30,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

France Portugal Switzerland — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN


0 0 0 0 0 0 1 2 4 9 16 29 47 76 131 252

471

740

1,109

1,907

4,196

0 0 0 0 0 0 1 1 3 5 8 13 22 38 72

155 32

0

585

1,011

1,819

3,612

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Norway Greece Scotland — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN

0 0 0 0 0 0 0 0 0 1 2 3 7 13 23

42

67

116

223

481

341

0 0 0 0 0 0 0 0 0 0 1 2 2 5 11

23

40

69

122

244

106

0

100

200

300

400

500

600

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

South Korea — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN


1 0 1 1 4 10 23 41 70 119

182

264 376 567 85

7

1,359

2,058

2,722

7,511

5,421

1,541

1 0 0 1 3 6 9 16 24 45 72 122

188 289 454 78

8

1,407

2,141

6,575

4,036

836

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Canada USA Mexico — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN

3 1 0 0 4 8 10 18 42 60 76

127 21

1 299

426

540

861

1,044

1,283

1,030

2,227

2 0 0 1 2 3 5 12 18 19 27 46 78 125 188 252

400 458

640

872

753

0

500

1,000

1,500

2,000

2,500

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Colombia — Rate of Death by Age Group and Gender (per million)

MALE FEMALE UNKNOWN


By now, it should be obvious that the very old age individuals experience by far the highest rates of infections as well as the

highest rates of death. That should of course not come as a surprise, since older people will have other health issues which will

affect their rates of survival. Although every life is important of course, it would be interesting to examine the statistics by

excluding the ages 80 and over, or another age bracket. It may be discovered that for a large majority of people, the COVID-19

would not qualify as a pandemic, but just another flu-like illness, while it would be qualified as a pandemic for the older

population.


21. AGE-SPECIFIC AND GENDER-SPECIFIC CASE FATALITY RATE

The CFRs are also showing some clear trends when it comes to comparing the differences by age.

0.0%

0.1%

0.1%

0.1% 0.1%

0.2%

0.2% 0.5% 0.8% 1.5% 2.5% 4.

5%

8.1%

14.2%

23.2%

33.0%

40.4%

45.5% 48

.3%

49.9%

51.0%

0.2%

0.2%

0.1%

0.1%

0.1%

0.1%

0.1% 0.2% 0.3% 0.5% 0.7% 1.5% 3.

3%

7.2%

12.9%

18.3%

22.1% 25

.1% 27.6% 29.8%

31.0%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Belgium Germany Italy Spain — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN

0.3% 0.5%

0.1%

0.1%

0.3%

0.4% 0.8% 1.6% 2.3% 4.3% 8.1% 15

.7% 31

.2%

60.9%

100.9%

127.1%

181.8%

216.6%

245.8% 26

2.5%

120.6%

0.1%

0.2%

0.2%

0.1%

0.1%

0.2%

0.4% 0.8%

1.0% 1.7% 3.0% 6.1% 13

.1% 26

.9%

46.5% 61

.2%

91.3%

110.3% 120.0% 13

8.4%

83.2%

0.00%

50.00%

100.00%

150.00%

200.00%

250.00%

300.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

France Portugal Switzerland — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN


0.0%

0.0%

0.0%

0.0%

0.1%

0.2%

0.5%

1.0% 2.1% 4.2% 7.6% 15.7%

30.5% 55.9%

104.9%

188.9%

302.4%

359.3%

360.0%

319.6%

107.7%

0.0%

0.0%

0.0%

0.0%

0.1%

0.2%

0.4%

0.8% 1.5%

2.4% 4.2% 8.9% 18.0%

36.0% 77

.7%

150.8%

243.5%

345.1%

478.1%

650.8%

386.0%

0.00%

100.00%

200.00%

300.00%

400.00%

500.00%

600.00%

700.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Norway Greece Scotland — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.1% 0.2%

0.3% 0.6% 0.9% 1.6% 3.3% 6.1% 10

.4% 17

.6%

26.0%

36.8%

50.8%

69.8%

100.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.1% 0.2%

0.0%

0.0% 0.2%

0.5% 0.8% 1.8% 4.7%

9.6%

15.0%

22.4%

31.9%

44.3% 50

.0%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

South Korea — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN


5.6%

2.1%

1.8%

2.0%

2.6%

3.9% 6.9% 10.4%

15.9%

25.2%

40.1%

64.1% 100.2% 16

2.5%

264.9%

330.2%

341.2%

326.9%

945.6%

734.7%

121.6%

5.3%

1.1%

0.3%

1.2% 1.7%

2.1%

2.9% 4.8%

6.6% 11.1%

18.3%

34.5% 65.0% 12

0.4%

214.0% 27

5.0%

274.6%

261.7%

699.4%

402.5%

70.3%

0.00%

100.00%

200.00%

300.00%

400.00%

500.00%

600.00%

700.00%

800.00%

900.00%

1000.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Canada USA Mexico — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN

0.5%

0.1%

0.0%

0.0%

0.2% 0.3%

0.4% 0.7% 1.7% 2.5% 3.3% 5.3% 9.

0%

14.8% 18.3% 21.9% 28

.9%

31.5% 37

.4%

38.7%

50.0%

0.5%

0.0%

0.0%

0.1%

0.1%

0.2% 0.3% 0.7% 1.1% 1.3% 1.9% 3.4% 6.1%

11.1% 15.5%

18.7% 24

.9%

25.8%

34.4% 41

.5%

100.0%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0 - 4 5 - 910 -

1415 -

1920 -

2425 -

2930 -

3435 -

3940 -

4445 -

4950 -

5455 -

5960 -

6465 -

6970 -

7475 -

7980 -

8485 -

8990 -

9495 -

99100

+

Colombia — Case Fatality Rate by Age Group and Gender

MALE FEMALE UNKNOWN


22. GENDER-SPECIFIC EXPERIENCE — INFECTIONS

By this time, it should be obvious that the rates of infections are more severe for women than men, at least in Europe and Asia,

and that the rates of death are more severe for men than women. The following charts show this relationship quite clearly.

MALE, 44.1%

FEMALE, 55.9%

Belgium Germany Italy Netherlands Spain — Distribution of Infections (758,587) by Gender

MALE, 44.7%

FEMALE, 55.3%

Portugal Austria Sweden Switzerland — Distribution of Infections (71,981) by Gender


In terms of infections, while the split male / female in Europe is mostly 55%/45%, the distribution in Asia, the Americas, and Africa

is closer to 50%/50% and even more male.

MALE, 50.2%FEMALE, 49.8%

Norway Estonia Greece Ireland — Distribution of Infections (13,796) by Gender


China Japan South Korea — Distribution of Infections (28,714) by Gender


MALE, 51.5%

FEMALE, 48.5%

Canada USA Mexico — Distribution of Infections (130,948) by Gender

MALE, 59.5%

FEMALE, 40.5%

Colombia Ecuador — Distribution of Infections (80,405) by Gender


MALE, 51.9%

FEMALE, 48.1%

Algeria Eswatini — Distribution of Infections (728) by Gender


23. GENDER-SPECIFIC EXPERIENCE — DEATHS

With respect to deaths, and based on the above samples, the distribution is most definitely biased towards males, with

approximately 55%. In South America, this proportion for males reaches 65.5%, or almost 2/3 of the total deaths.

MALE, 56.4%

FEMALE, 43.6%

Belgium Germany Italy Spain — Distribution of Deaths (79,583) by Gender

MALE, 58.2%

FEMALE, 41.8%

France Portugal Switzerland — Distribution of Deaths (22,288) by Gender



Norway Greece Scotland — Distribution of Deaths (4,505) by Gender

MALE, 53.8%

FEMALE, 46.2%

South Korea — Distribution of Deaths (282) by Gender


MALE, 55.0%

FEMALE, 45.0%

Canada USA Mexico — Distribution of Deaths (105,769) by Gender

MALE, 65.5%

FEMALE, 34.5%

Colombia — Distribution of Deaths (2,640) by Gender


24. EXCESS MORTALITY

With more and more data being available, it is now easier to discern excess mortality. The excess mortality is defined as the

extra deaths related to infections by COVID-19.

In the graphs on the following pages, we show by country line graphs that have the following rates over a period of weeks in

2020:

• The average rates of mortality over the last few years (green line).

• The 99.5% confidence interval of the rates of mortality (dark shaded and wide green line).

• The 95% confidence interval of the rates of mortality (light shaded and wide green line).

• The forecast rates of mortality for 2020 (blue line). There seems to be inconsistencies for some countries for

this measure. These will be investigated.

• The actual rates of mortality for 2020 (red line). This is where for some countries we see a drastic increase,

like two times the average mortality in the United Kingdom.

• The ratio of actual rates to the average rates (dotted red line). The scale here is based on the axis on the right

side of the graph.

It is interesting to see the countries that have much higher mortality than the average and those that do not. These observations

are consistent with everything that has been reported previously in this report.


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COVID-19 - EXCESS MORTALITY (26-Jun-2020 - Day 177) - US — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd



25. MORTALITY FORECAST

This will be a relatively short section, almost it must be addressed. As the saying goes, let not avoid the elephant in the room.

Can we forecast the mortality rates due to COVID-19. The answer, in our opinion, is clearly “no” and for several reasons:

(1) Although general mortality varies across the world, it is generally comparable across countries at least within

a narrow margin. Let us assume a very conservative wide margin of 20%. The observed range of COVID-19 by

country is extremely wide, for example Iran at above 100 per million, Saudi Arabia at above 30 per million,

New Zealand at approximately 4.5 per million, Taiwan at approximately 0.30 per million. So obviously, there

are a large number of factors that affect COVID-19 mortality. One would need to first identify these factors,

and then find the correlation, which is nearly impossible. Even with a homogenous group like the Nordic

European countries, Sweden stands on it own with much higher level and clearly as a result of its lax

response.

(2) Even within one country, the apparent excess mortality is quite different. For instance, we can look at New

York State versus North Dakota (ratio of 16 to 1), the province of Québec versus its neighbour Ontario (a ratio

of 3.5), the region of Lombardy versus the rest of Italy, the province of Wuhan versus the rest of China, etc.

(3) Mortality tables are based on large amount of data over several years. The COVID-19 pandemic has been with

us for only a few months. As the experience is shifting all the time, there is no possibility of finding a stable

environment.

(4) The general mortality is very sticky or resilient. In the last 200 years, we have seen an overall mortality

improvement of 1% to 2% annually. The mortality very rarely takes a bad turn. The Spanish Flu is a good

example of such a bad turn. Sometime, it takes a good turn. The invention of penicillin and modern vaccines

have clearly caused a good turn. These changes take years and sometime decades to manifest themselves.

On the other hand, the COVID-19 experience is extremely volatile. For example, the US had new deaths of

almost 2,500 per day a few weeks ago, and now it is at a 750 level. Brazil went the other way, from about 500

to 1,250. These large swings make it impossible to narrow down the numbers.

(5) Even if we wanted to concentrate on the Case Fatality Rate, there is also a very wide range across countries,

states, provinces, and regions. The health care systems are clearly part of the answers, as well as the

countries and local government’s response.

All of the above variables, in our opinion, make it impossible to forecast mortality due to COVID-19. And once COVID-19 vaccines

become available, whatever attempt to forecast mortality will become futile.


26. SOME OVERALL OBSERVATIONS AND CONCLUSIONS

With so many datapoints, we are overwhelmed with the amount of information provided, and sometimes contradictory

information. Irrespective of the number of datapoints, we must remain conscious that there are some obvious errors or

distortions in the data. This could be due to under-reporting or mis-reporting. There are also different approaches in reporting

cases. For example, for infections whether it is considered severe or mild, symptomatic or asymptomatic. For deaths, whether

those having COVID-19 die of it or due to causes triggered by it. And of course, there is the availability of the data itself, which in

some countries with rural areas or large populations can be difficult. India (1.38 billion) and Africa (1.34 billion) come to mind.

But these limitations are true for other data gathering too, whether they are economic or demographic. So, we must make the

best use of the information that we have and always keep a critical view without judgement.

Notwithstanding, we reach a point in the data analysis where there are some clear observations and conclusions:

(1) The first thing that strikes us is the great disparity of the observations by country. We can safely conclude that

some countries reacted better than others in controlling the outbreak and in caring for the sick. In otherwise

very similar countries like Norway and Sweden, the differences are striking. Whether Sweden’s strategy in

being light in controlling the outbreak for the objective of developing some immunity is correct, we have yet

to see positive results. Maybe that will be demonstrated if there is a second wave of COVID-19.

(2) The disparity by continent or part thereof is also striking. With some exceptions, we can observe that Asia is

somewhat under control, that Europe has stopped the outbreak, and that Africa is still very low compared to

the rest of the world. There are developing trends however in South America with Brazil leading the infection

rates.

(3) In general, the older population is much more affected. However, this is not entirely true and South Korea is

an example where clearly all ages are affected. On the death toll however, then the elderly population is

significantly more affected than younger people.

(4) Overall, women seem to be more susceptible to COVID-19 than men, roughly in the range of 55%. However,

much more men die of COVID-19-related conditions, approximately 55%-60% and as much as 2/3 in North

America.

(5) At first glance, there appears to be some contradictory observations. For example, the death rates in some

European countries have actually decreased in younger people. One cause for this is social isolation and

lockdown has reduced the number of fatal car crashes where younger people, especially males, are more

often affected. Since the younger population is also less affected by COVID-19 and also very few die as a

result, this results in some reduction in mortality.


27. OTHER INTERESTING SOURCES

27.1. i.e. Muhanna & co, Actuaries & Consultants

Our colleagues at i.e. Muhanna & co in Lebanon have developed an interactive website on the evolution of the COVID-19

pandemic. The site is updated daily based on UN / JHU data.

You can visit the site at: http://muhanna.com/en/research/

For more information, please contact Michael Muhanna at: [email protected]

Here are some screenshots from the site, reproduced with permission.


http://muhanna.com/en/research/

mailto:[email protected]?subject=COVID-19

27.2. Our World in Data

The following website is very well done and provide full interaction: https://ourworldindata.org/coronavirus-data-explorer?

zoomToSelection=true&deathsMetric=true&dailyFreq=true&aligned=true&smoothing=7&country=&pickerMetric=location&pickerS

ort=asc


28. COVID-19 — BEYOND THE DATA

28.1. A Personal Account of Navigating the New Normal of a Pandemic Lockdown

In this section, we asked our co-authors and some colleagues to share with our readers their personal experience with

COVID-19. How did it change their life, temporarily or permanently? What impact has COVID-19 had on their family and close

friends? How did it affect their business? Each collaborator was left to their own way of expressing themselves.

These views are a personal perspective from the authors of this report to try give a human touch to the many pages of statistics found in this

document. These views do not necessarily represent the views of our employers and are contributed in a personal capacity.

28.2. Ron Richman — Associate Director, QED Actuaries and Consultants

The case of South Africa

I have begun writing this article on the evening of 4th July 2020, which is the 100th day of South Africa’s lockdown which began

on 26th March 2020. Although current events would have seemed surreal a few months ago, the current reality of living in a

world grappling with COVID-19 seems to be something that will affect the short and mid-term future.

When reports of an outbreak of a new virus in Wuhan, China, began to circulate in January 2020, it seemed that there was a

possibility that this would remain localized. The possibility of a pandemic became clearer to me personally when I read reports

about the high attack rate of the virus in early publications emerging from China and the risk management implications in an

article by Norman, Bar-Yam and Taleb which was published on 26th January a few weeks before cases were reported in South 1

Africa. However, the impact became clearer when the first case of COVID-19 was reported in South Africa on 5th March 2020.

These first infections were mainly imported cases, meaning to say, South Africans had caught the disease in Europe and

returned as carriers. It seemed for a while as if community transmission of the virus could be contained, especially if action was

taken early enough. South Africa has had a long experience dealing with communicable diseases, with tuberculosis and HIV/

AIDS prevalent in the population. No doubt, this long history has guided the public health response to COVID-19. Indeed, the

government acted strongly, and a full lockdown was put in place on 26th March 2020, that was intended to give the public

health authorities sufficient time to prepare facilities for the expected burden of patients.

Since that time, the pandemic has progressed and in recent weeks, the 100,000-infection mark was passed, with 200,000

infections already in sight as of the writing of this report. In a population of about 60 million people, this means that about 3 in

1000 are infected. At the same time, some elements of the lockdown have been eased to allow the economy to start functioning

again.

Joseph Norman, Yaneer Bar-Yam, and Nassim Nicholas Taleb, Systemic risk of pandemic via novel pathogens – Coronavirus: A note, New 1

England Complex Systems Institute (January 26, 2020).


At the present time, quite severe outbreaks of COVID-19 are underway in three provinces of South Africa: the Western Cape,

Gauteng and the Eastern Cape. While South Africa has had longer to prepare than many other countries, emerging evidence

shows that comorbidities such as TB and AIDS may increase the risk of a fatal outcome from COVID-19. Thus, like several other

developing countries, the outlook for the short term is worrying.

Professional Implications

QED reacted to the lockdown quite early on by testing the company’s business continuity strategy in February. As a firm that has

allowed and encouraged remote working before 2020, we were well placed to transfer to full-time remote working, and this

decision was taken before South Africa’s formal lockdown. We are still mainly working from home but have found new ways of

connecting with applications such as Microsoft Teams becoming the standard method of chatting and making intra-company

calls. The greater dependency on technology means that, in common with other organizations, our immediate awareness of

cyber risks and reliance on our internet supplier has increased. Personally, I miss the daily interactions and water cooler

discussions with my colleagues in the office and that is one aspect of the old way of working that is not emulated well by the

new virtual office. Nonetheless, our firm’s staff seems to have adapted well, although some of the more junior staff have felt the

lack of face to face interaction more keenly.

The impact on the insurers we serve has generally been on operations and profits, though, in most cases, there has not been an

adverse impact on solvency. Exposure to markets meant that the high asset volatility in March affected balance sheets, but the

recovery since then means that at least part of these losses has been reversed. For Life clients, we have seen a significant

impact on retrenchment claims as the economy has contracted, but fewer accidental death/disability claims. In the general

insurance market, most motor lines have experienced lower losses whereas business interruption claims have impacted some

property lines. A general theme has been the difficulty of setting assumptions in this challenging environment.

As an actuary with a particular interest in demography, I was interested in understanding what the data would tell us about

COVID-19 and started working with the available data from John Hopkins and other sources. Part of the challenge of

understanding COVID-19 is that not all statistics are comparable (e.g. different testing practices in different countries or even in

different parts of the same country), or of a comparably high quality. Thus, within this report, we have made efforts to validate

the data, such as by calculating the number of excess deaths using forecasting methods.

Working with this data gave rise to the opportunity to collaborate with actuaries from Eckler Ltd in Canada to produce the now

weekly report containing COVID-19 data from across four different databases. This experience of collaborating outside our firm,

with actuaries from a different country, is somewhat reflective of another trend: working with colleagues, collaborators and

friends from around the world seems to have been normalized during these last few months. New communities have sprung up

– such as the One World Actuarial Research seminar, which hosts presentations on actuarial topics every few weeks by

presenters from around the globe.


What Is Next?

One thing is clear – the experience of COVID-19 will have far reaching implications in our personal and professional lives. From

a personal perspective, hopefully, we will walk away strengthened from the experience of alternative ways of working and

serving our clients and communities. Professionally, in actuarial practice, we often derive sets of assumptions based on past

data, but COVID-19 has been an almost unique event, with the last comparable data probably from the Spanish influenza

outbreak from more than 100 years ago. Thus, our experience of working under conditions of extreme uncertainty will probably

lead to more detailed consideration of the best ways for allowing for and making decisions under similar conditions.


28.3. Sylvain Goulet — Eckler Ltd.

The Ides of March

We originally heard about the COVID-19 outbreak in the Chinese province of Wuhan back in early January 2020. It was not until

late February and early March, when the outbreak reached Europe and it rapidly spread throughout Italy, Spain and France, that

it became evident we were dealing with a pandemic.

The month of March began leaving its trace dramatically with new infections accelerating exponentially. The Ides of March is the

74th day in the Roman calendar and corresponds to March 15th. It was traditionally marked by religious observances, although in

44 BCE it took on more relevance as it is the date of Julius Caesar’s assassination at the hands of his fellow senators who

stabbed him at least 23 times. The Ides of March – or March 15th – is now famous for something else – the COVID-19 pandemic

of 2020 showing its real ugly face.

Self-Imposed Lockdown

On the heels of the World Health Organization declaring COVID-19 an actual pandemic, Eckler’s Risk Management Committee

took the decision to temporarily close all physical offices, starting on 16th March 2020. Within just a few days, we were able to

re-locate staff in Canada, Barbados and Jamaica, to home offices without skipping a beat. As I write this, it has been three and

half months since that closure. From the beginning of this situation, I made it a point that our group of consulting actuaries and

technology gurus should meet by video conference once a week. My objective was to ensure that we remain connected to

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Comparison of New INFECTIONS Analysis — (Europe 1) — (from 01-Jan-2020 to 03-Jul-2020 - Day 184) — © 2020 ECKLER LTD and QED Actuaries & Consultants (Pty) Ltd

France

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United Kingdom


each other throughout the lock down. Electronic communication like email and espace™, our proprietary online collaboration

tool, are great for communication, but when you cannot meet in person, the next best thing is to meet by video.

Our group at Eckler has always been on the leading edge of technology. We have worked remotely for years either once or

twice a week or, in my case, when I travelled extensively. So, operating remotely has not been an issue from that standpoint.

However, I have found that the lack of direct human interaction has been harder than I had anticipated.

Back in mid-April, during one of our group video calls, we made bets as to when Eckler would officially re-open its Toronto

office. Two of us, including me, suggested July 2nd. As July 1st is Canada Day (a national holiday), it seemed fitting that we would

return to the office after celebrating the country’s birthday. Most of the other staff suggested dates falling between August 1st to

September 1st, with one notable exception. Our one outlier suggested we would not return until 2021. Of course, we all chuckled

at the thought. And yet, today with July 1st now passed, I am beginning to think that mid-October 2020 (Canadian Thanksgiving)

may be the likely time we finally return to the office. That is not so long to 2021.

Working from Home

Canada, and the province of Ontario where I am based, were never in complete lockdown like Spain or Italy. But travel

restrictions and social distancing became the new norm, as well as wearing masks and gloves when the situation requires it. My

wife and I live on 110-acres of farmland 130 kilometres from Toronto, Canada’s largest city. So social distancing is not an issue for

us around our home and on the land. We are blessed to be in this situation, and I do not envy the people of Toronto living in

dense places with parks closed. Ironically, I believe that most people in Toronto feel more isolated than us.

My commute to work used to be 260 kilometres round trip. I have an electric car and I used the toll road which is six lands in

some places and wide open. This allows me to travel fast for 45 minutes with the other 30 minutes on county roads. So, it’s

actually quite pleasant and when compared to some of my Toronto colleagues who experience longer commutes due to stop-

and-go traffic during rush hour, I again consider myself fortunate.

People often ask me why I don’t work at home more often. I have a great home office on the second floor of our house, but it is

open with a high sloping ceiling over the kitchen, living room and dining room. That means when there is a lot of activity

downstairs, I have a harder time concentrating upstairs. And given my wife is like a cat with nine lives; or, in her case, nine (or

close to it) occupations — Yoga Teacher, DEI Trainer, Workshop Leader, Life Coach, Voice Artist, Performer (theatre), Farmer —

the house is always a hub of activity. There is a constant flow of phone calls, workmen to do some heavier work, gardeners,

grass cutters in the summer and snow blowers in the winter, yoga classes, cooking (my dear wife is a wonderful cook but a noisy

one!), and so on. I am fortunate, again, that I am able to tune out the noise, although it is not ideal. Things get worse when we

lose the internet — a common occurrence when you live in the country! The only time our home is quiet is when my wife

decides to take a break — which is not very often. Although I love to be with my wife more or less 24 hours a day, it is worth my

drive to Toronto to get more work done.

Well… I have been continually at home now for almost 120 days, I have had to adjust to this new routine. I used to be at the

office from 0600 to 1800. Now, I am at my home office from 0700 to 1900. However, there are many more interruptions. To be


fair, there were many interruptions at the office as well, but home interruptions are usually caused by something that goes

wrong and needs fixing… right away.

Travel — Going, Going, Gone

Our clients and colleagues are international — Barbados, Trinidad, Jamaica, Bermuda, Florida, London, Paris, Zurich, South

Africa, Mauritius, Hong Kong, and many other far away places. So, I used to travel a lot for business. That came to a complete

stop in March and, honestly, I have no desire to board a plane in 2020. I believe that 2021 will return to a more normal situation,

although wearing masks while navigating airports and on planes will likely become the standard. However, I am meeting clients

and colleagues more often now, not in person, but through video conferencing, which was an adjustment, too. Every client and

colleague uses different tools, so we have done our best to adapt to what works. I consider Zoom and WebEx to be the best for

our business. Microsoft Teams work fine if all parties have it. FaceTime is great for personal calls as it is simple and mainly

geared to meeting face to face.

Leaving the House

My wife and I stay pretty close to home. We try to do grocery shopping only once a week. We keep it to a minimum, make a list

before we go and then zoom through the stores with our face masks and gloves, so we can get out as quickly as possible. Some

people respect social distancing, but many others disregard the protocols leaving the impression they do not believe in their

effectiveness. From my perspective, it is not so much that I would be concerned about shaking hands with friends and getting

close, but more the fact that if everyone did this, then you cannot easily stop the transmission of viruses. I have seen many

reports that the incidence of the common flu in late winter and early spring this year has been significantly down. There is no

doubt that social distancing has contributed to this.

While we miss our family and friends, my wife and I have adapted to social distancing. We have had a few virtual dinners with

friends using Zoom and FaceTime. Although it is a bit unusual, we got used to it. We still interact and have lively conversations,

and sometimes we even have similar food and wine, which makes it fun.

What Is Next?

As I write this on July 3rd, the world has now passed 11.3 million infections and over half a million deaths. Looking at the global

numbers, it looks very much like a game of musical chairs with the lead countries switching places. However, there seems to be

little doubt that the United States will secure the number one spot. As the most powerful country in the world, this is not a point

of pride. Although, the United States has the financial power to counteract the impact. Unfortunately, the runner-up countries

— Brazil, Russia and now India — may not be so fortunate.

For life to return to some normality, I am of the view that a vaccine will be required. Until such time, it may take six to 24 months

to reach a new level of normality. I have no doubt that the world will survive and thrive again. World War I and the follow-up

Spanish Flu pandemic were devastating occurrences just over 100 years ago. The world rebounded stronger than before,

proving with certainty that the human race is extremely resilient.


A. LIST OF COUNTRIES AND MAJOR DATA POINTS


statistical perspective of covid-19 and analysis data...

Documents