effectsoflocation-specificmeteorologicalfactorsoncovid-19...

Research ArticleEffects of Location-Specific Meteorological Factors on COVID-19Daily Infection in a Tropical Climate A Case of KualaLumpur Malaysia

Ezekiel Kaura Makama 12 and Hwee San Lim 1

1School of Physics Universiti Sains Malaysia 11800 USM Pulau Pinang Malaysia2Department of Physics University of Jos PMB 2084 Jos Nigeria

Correspondence should be addressed to Hwee San Lim hslimusmmy

Received 11 December 2020 Accepted 19 March 2021 Published 10 April 2021

Academic Editor Pedro Jimenez-Guerrero

Copyright copy 2021 Ezekiel Kaura Makama and Hwee San Lim is is an open access article distributed under the CreativeCommons Attribution License which permits unrestricted use distribution and reproduction in any medium provided theoriginal work is properly cited

Insufficient information on the novel coronavirus (COVID-19) has made it more difficult for the world to tackle its continuousimplosion Meteorological and environmental factors in both laboratory and epidemiological studies have been reported to affectthe survival and transmission of the virus In this study the possible effects of location-specific meteorological parameters in atropical climate on new daily COVID-19 infection (NDI) are investigated in Kuala Lumpur from 14 March 2020 to 31 August2020 A generalized additive model (GAM) was imposed on ambient temperature (T) and absolute humidity (AH) to explore theirnonlinear relationship with NDI Piecewise linear regression was then used to further discern the relationships below and abovethe threshold values of both T and AH e relationship between T and NDI which was linear and statistically significant forTgt 297degC showed that each unit rise in temperature increases NDI by about 3210 (CI 1372ndash7976) AH had a morepronounced linear association with NDI for AH le 226 gm3 but tended to flatten the exposure-response curve above this value A1 gm3 increase in AH increases NDI by 3807 (CI 2064ndash5732) Generally the results indicated a positive association between Tand NDI particularly above 297degC while the association with AH showed a stronger positive relationship below 226 gm3 eimplication of this is that COVID-19 could not be suppressed on account of warmer weather as such public health interventionsremain imperative

1 Introduction

Earlier in 2020 cases of a novel coronavirus were witnessedacross China after which the World Health Organization(WHO) on 31 December 2019 announced it was in receipt ofinformation on an epidemic with unidentified etiologyoriginating from Wuhan China [1 2] e novel diseasewhich is associated with the Severe Acute Respiratory Syn-drome Coronavirus 2 (SARS-CoV-2) is acknowledged to behighly infectious with consequent public health emergenciesOnMarch 11 2020WHO declared SARS-CoV-2 a pandemicdue to its high transmissive nature with increased globalmobility and officially renamed it COVID-19 [3]

COVID-19 is believed to be transmitted directly from ahuman to another through respiratory droplets from an

infected person (sneezing coughing laughing and to alarge extent talking) to a healthy person within a range ofone metre A healthy person can also be indirectly infectedvia contaminated surfaces [1 4 5] e maintenance ofsocial distancing of at least one metre the avoidance ofnose- mouth- and eye-touching and frequent handwash-ingsanitizing are therefore important steps to mitigate theinfection rate [3] According to clinical diagnosisCOVID-19 patients present symptoms similar to othercoronaviruses like severe acute respiratory syndrome(SARS) and Middle East Respiratory Syndrome (MERS) [6]Although it is widely acknowledged that COVID-19 istransmitted through human respiratory droplets and directcontact the potential for viral aerosol transmission and therole of weather parameters is an ongoing debate

HindawiAdvances in MeteorologyVolume 2021 Article ID 6675943 10 pageshttpsdoiorg10115520216675943

Many studies have associated the spread of COVID-19with a wide range of factors among which are weath-er-related conditions [7ndash10] including environmental fac-tors such as air pollution index (API) [11 12] e survivaland transmission of the virus by droplets from infectedpersons are considered to be optimal in dry and cold weatherconditions [13] Studies investigating the effects of meteo-rological conditions on COVID-19 transmission since thebeginning of the year 2020 have largely been conducted inChina [14 15] Iran [16] Europe and the United States ofAmerica [17] ese reports and others have contradictoryconclusions on the relationship between meteorologicalfactors and COVID-19 transmission For instance Liu et al[14] based on data from 130 Chinese cities between 20January and 2 March 2020 concluded that lower temper-atures and humidity favor its transmission However Xieand Zhu [15] who investigated the relationship betweendaily mean temperature and newly confirmed COVID-19cases in many cities in China from 23 January to 29 Feb-ruary 2020 reported no evidence to suggest that case countswill decline in warm weather Yao et al [18] on the otherhand using the cumulative confirmed cases for 224 Chinesecities as of 9 March 2020 claimed the nonexistence ofcorrelation between COVID-19 and temperature or UVradiation However Bukhari and Jameel [17] analyzed thepatterns of local weather of the global regions affected byCOVID-19 and reported that most of the regions werewithin a certain range of temperature and absolute humidityis is corroborated by Gupta et al [8] who studied the effectof weather on COVID-19 in the United States (US) between1 January and 9 April 2020 Temperature and absolutehumidity have generally been reported as crucial weatherindices associated with the spread of COVID-19 eg [8 17]

Apart from the contradictory results on weath-er-COVID-19 relationship it can be argued that the re-ported association could differ in tropical climates whereboth temperature and humidity are almost uniformly highall year round with consequent low variations More so thestudies above covered very short periods of investigationwhich has the tendency to impair the outcome of theseinvestigations Higher rates of COVID-19 in countries likeIran Italy and South Korea and in some states in the USshare a similar temperature range of between 3 and 10degC[8 17] with Hubei in China the origin of the disease elower number of COVID-19 cases in Southeast Asiancountries (eg Malaysia Singapore and ailand) is at-tributed to warmer temperatures in the region [17] It couldtherefore be inferred that besides mobility and quaran-tining other factors are inimical to rising cases of COVID-19in a location

In this study the effects of location-specific weatherparameters on new daily COVID-19 confirmed cases inMalaysia are explored is is motivated by the significantdifferences in the growth rate of the disease found in dif-ferent countries and cities which is mostly attributed todifferent climatic features More so very few evaluationsbetween weather parameters and the evolution ofCOVID-19 exist for the tropical climate regions [9 11 19] Itis imperative therefore to investigate if tropical weather

parameters slow or aggravate the spread of the virus estudy specifically explores the association of temperature (T)and absolute humidity (AH) with new daily confirmedCOVID-19 infections (NDI) in Kuala Lumpure choice ofthe study area is informed by its tropical location highercases of the disease and its population density

2 Data and Methods

21 Study Area e first COVID-19 cases in Malaysia wererecorded on 25 January 2020 in Johor Bharu when someforeigners whose destination was traced back to China [20]were symptomatic e infection remained very low andlargely confined to imported cases not until the emergent oflocalized clusters which spiked due to a religious gatheringin Kuala Lumpur earlier in March 2020 e total number ofinfections in Malaysia from 14 March 2020 to 31 August2020 stood at 9353 with Kuala Lumpur Selangor NegeriSembilan Johor Sarawak and Sabah recording higher casesin that order Kuala Lumpur along with areas mentioned had7963 cumulative infections representing over 85 ofconfirmed cases in Malaysia within this period (seeFigure 1)

Kuala Lumpur the capital city of Malaysia is inwest-central Peninsular Malaysia on latitude 3141degN andlongitude 101687degE It is the countryrsquos largest urban areaand its cultural commercial and transportation centere climate is Af (tropical rainforest) under the Koppenclimate classification with high temperatures and hu-midity that vary little throughout the year e arithmeticmeans of maximum average and minimum temperaturesare respectively 35 29 and 234degC with an averagerelative humidity of 80 e area receives about2400mm of rain annually with June and July as the driestmonths Being a capital city Kuala Lumpur is the maindestination for investors job seekers and tourists It istherefore the most densely populated city in Malaysiawith about 8 million people residing within 243 km2which represents 314 annual growth rate (httpsworldpopulationreviewcomworld-citieskuala-lumpur-population) Analyzing the spread of COVID-19 trans-mission in relation to weather and environmental vari-ables in Kuala Lumpur is important because of itseconomic relevance and population density More soAuler et al [11] and Bashir et al [12] have emphasized onthe importance of evaluating weather parameters in adensely populated environment As of 31 August 2020 thecumulative COVID-19 confirmed infection in KualaLumpur stood at 2587 the highest in the country asshown earlier in Figure 1

22 Data Daily records of mean ambient temperature (degC)wind speed (ms) and relative humidity () for the period14 March to 31 August 2020 were retrieved from the ar-chives of Weather Underground available online at httpwwwwundergroundcom is popular and reliable onlineresource for meteorological data has been used extensivelyfor atmospheric research applications [7 19 21 22] AH

2 Advances in Meteorology

which defines the mass of water vapor per volume of air (gm3) was considered because it is regarded as a better in-dicator of humidity in acute health effects [8 23 24]Shaman and Kohn [24] reported that whereas AH canrespectively explain 50 and 90 variations in influenzavirus transmission and its survival H can only explain 12and 36 and that the epidemic influenza usually peaksduring winter when low AH maximizes Some studiestherefore support the epidemiological notion that low AH(cold and dry weather) promotes the growth of droplet-mediated virus while higher AH (warm and humid con-ditions) attenuates the viral transmission [24 25] Dailymean value of AH (gm3) was therefore computed for thestudy period using Clausius-Clapeyron relationship given inequation (1)

AH 21674H6112e

(1767TT+2435)

T + 27315 (1)

where T and H are the temperature and relative humidityrespectively

Hourly API data which is based onmean concentrationsof PM10 O3 CO SO2 and NO2 was obtained from the airquality division of the Malaysian Department of Environ-ment e hourly API data for Kuala Lumpur from the 2background monitoring stations (Batu Muda and Cheras)covering the study period was extracted and rescaled todaily means for compatibility with COVID-19 daily casesSecondary data on COVID-19 cases (cumulative and daily)for the statescities inMalaysia was obtained from the officialwebsite of the Ministry of Health Malaysia httpcovid-19mohgovmy from which the data for Kuala Lumpur wereextracted

23 Statistical Evaluation Descriptive analysis was per-formed for all datasets Since the main objective of this studyis to explore the association of COVID-19 new daily in-fection hereafter NDI with weather variables Spearmancorrelation a nonparametric test was deployed Spearmanrsquosrank correlation coefficient or rho (rs) determines the

direction and strength of the monotonic relationship be-tween two variables and can be estimated using equation (2)

rs 1 minus61113936 d

2i

n n2

minus 11113872 1113873 (2)

where di is the difference between the two ranks of eachobservation and n is the number of observations A perfectlypositive or negative Spearmanrsquos correlation coefficient isobtained when rs plusmn1 We used the generalized additivemodel (GAM) to study the effects of weather factors on NDIGAM is a semiparametric extension of the generalized linearmodel often used to explore the nonlinear relationshipsbetween weather parameters and health-related issues[15 26 27] Since the effect of weather on COVID-19 couldlast several days [20] we considered a moving averageapproach to accommodate the cumulative lag e movingaverage lag effect (lag0ndash7 lag0ndash14 lag0ndash21 and lag0ndash28) ofT and AH on NDI was therefore examined

GAM with a Gaussian distribution link function inwhich the basic model for NDI was first built without themeteorological variables was implemented We incorpo-rated smoothed spline functions of time which accom-modates nonlinear and nonmonotonic patterns betweenNDI and time thereby offering a flexible modelling tool Ameasure of how well the model fitted the data was deter-mined by Akaikersquos information criterion e penalizedsmoothing spline function was used to control the effects ofconfounding factors like time trends day of week (DoW)and API after which the meteorological factors were in-troduced and their effects on NDI analyzed in accordancewith some time-series studies [27 28] e model was de-fined in equation (3)

logE(Y)t β0 + βXt + s(t k df + 1)

+ s(API k df + 1) + DoW(3)

where E(Yt) is the expected number of NDI Yt is the NDIfor day t and t is the day of observation β0 and β are theintercept and coefficient of regression Xt is the value of the

8degN

6degN

4degN

2degN

100degE 104degE 108degE 112degE

150 75 0 150

Total COVID-19

N

lt200200ndash699700ndash9991000ndash1999

2000ndash2500

gt2500300km

116degE 120degE

Figure 1 COVID-19 cumulative confirmed cases across states and cities in Malaysia as of 31 August 2020 Kuala Lumpurrsquos cumulative caseis classified as the red disc

Advances in Meteorology 3

meteorological parameter on day t and s(middot) is the smoothfunction of time and API variables e statistical analyseswere two-sided at a 95 interval and were performed usingthe statistical software R (version 353) with mgcv (version18ndash27) packagee effect estimates per one unit increase inthe meteorological variables associated with NDI wereexpressed as percentage changes with their 95 confidenceintervals (CIs)

3 Results and Discussion

31 Descriptive Analysis of COVID-19 Cases and WeatherVariables Daily variations of weather parameters used in thisstudy from 14 March to 31 August 2020 and their descriptivestatistics are respectively presented in Figure 2 and Table 1Within this period a cumulative number of 2587 confirmedcases of COVID-19 were recorded in the study area duringwhich temperature ranged from 257degC to 334degC with anaverage of 2950plusmn 113degC and a mean daily variation of77plusmn 159degC e narrow variation in temperature is to beexpected in a tropical climate where seasonal distinction is notas conspicuous as obtained in the mid and high latitude re-gions Wind speed was between 001plusmn 004 and 481plusmn 115mswith an average speed of 156plusmn 047ms Large daily variation(208 gm3) of absolute humidity was obtained where themeans were confined within 1390plusmn 206 and 347plusmn 296 gm3with the overall average being 227plusmn 106 gm3 Kuala Lumpurwithin the study period presented moderate API as seen inTable 1 with maximum average and minimum daily meansbeing 69 55 and 38 respectively Although the air qualitywithin the period of investigation was generally moderate inthe area the values were still higher than those obtained inmost cities across Malaysia (not shown)

Cumulative infections and NDI in Kuala Lumpurfrom 14 March to 31August are shown in Figure 2(a) NDIshowed very high variation probably due to sub-notification cases of the pandemic in Malaysia isconclusion is borne from the fact that there was no sta-tistical mode for the registered NDI as shown earlier inTable 1 e number of new cases followed the same trendwith the cumulative infections until 13 April when thetrend became distorted particularly from 4 June to31August except for some spikes on 25 May and 3 JuneFor instance the highest cumulative case on 31 Augustmatched zero new daily infections Daily infection peakedon 3 June 2020 with 204 NDI resulting in 2288 cumu-lative cases after which the number dropped drasticallyflattening the cumulative curve e drop in reportedinfection is perhaps due to measures adopted by theMalaysian authorities during which quarantining andpublic lockdown were enforced leading to the declarationof movement control order (MCO) As of 31 August 2020there was no new infection and cumulative infection was2587 e relatively large number of COVID-19 cases inKuala Lumpur being the main economic hub for inves-tors job seekers and tourists is to be expected due to itsdense human population [17 29] among other factors likeweather

32 Relationship between MeteorologicalEnvironmentalVariables and NDI Temperature wind speed absolutehumidity and air pollution index from 14 March 2020 to 31August 2020 have been correlated with NDI in KualaLumpur Although the explanatory variables (meteorolog-ical and environmental) showed normal univariate distri-bution based on their kurtosis and asymmetry seen earlier inTable 1 which were within plusmn2 [30] we opted for a non-parametric approach to avoid possible autocorrelation issueSpearmanrsquos rank correlation test was therefore imposed onthe variables and the results are summarized in Table 2 Tand AH showed respective significant positive correlationsof 038 and 064 with NDI at the 1 level Strong negativeassociation was however observed for u (ndash036) and API(ndash035) also at the 1 level

33 Effects of Meteorological Variables on NDI e rela-tionship between meteorological variables and NDI aftercontrolling the effects of API and u is shown in the ex-posure-response curves depicted in Figure 3 Generally therelationship between T and NDI seen in Figures 3(a) 3(c)3(e) and 3(g) suggests a positive and significant nonlinearassociation (plt 005) except for lag0ndash28 where the asso-ciation is somewhat linear A more conspicuous nonlinearinfluence on NDI was obtained for AH as seen in Figures3(b) 3(d) 3(f ) and 3(h)e initial positive response of NDIto increase in AH was linear until at a threshold value of2260 gm3 when the relationship became nonsignificantresulting in the observed flattened curve thereafter

e model was subjected to sensitivity test by alteringthe degree of freedom (df ) of the penalized smoothingspline function for calendar time as well as changing the dfbetween 3 and 6 for both Tand AH Based on the nonlinearGAMs relationship between NDI and the meteorologicalvariables we carried out piecewise linear regression tofurther evaluate their effects in this sensitivity analysis Apiecewise linear regression was conducted using the re-spective threshold values of 297degC and 226 gm3 to esti-mate the effects of Tand AH on NDI above and below theseknot points e result of the piecewise regression aftercontrolling u and API at the respective break points of Tand AH for all the lags including their correspondingconfidence intervals (CIs) at the 95 level is presented inTable 3

It was observed that a 1degC rise in T with its valuele 297degC led to corresponding changes of 1024 (CI0721ndash2864) and 1462 (CI 0191ndash3170) in NDI for lag0ndash7and lag0ndash28 respectively However within this temperaturerange the positive effect of T on NDI was statisticallynonsignificant for lag0ndash14 and lag0ndash21 Higher significantand positive responses in NDI for each 1degC rise in T wererecorded for values above 297degC in all the lags with lag0ndash28presenting the greatest effect of 3210 (CI 1372ndash7976)e result also showed that the effect of absolute humidity onNDI at AH le 226 gm3 is generally positive and stronglysignificant at the 95 level e greatest effect was observedin lag0ndash28 with 1 change in AH resulting in a 3807 (CI2064ndash5732) increase in NDI Although the effect of AH on


CumulativeNew daily infection

21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

1000

2000

3000

Cum

ulat

ive C

OV

ID-1

9

0

50

100

150

200

New

dai

ly in

fect

ion

(a)

TemperatureAbsolute humidity

21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

15

25

35

Tem

pera

ture

(degC)

and

abso

lute

hum

idity

(gm

3 )

(b)

Air pollution indexWind speed

21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

2

4

6

8

Win

d sp

eed

(ms

)

25

40

55

70

Air

pollu

tion

inde

x

(c)

Figure 2 Daily variation of weather parameters and COVID-19 cases from 14 March to 31 August 2020 for Kuala Lumpur (a) Cumulativeand daily COVID-19 spread (b) mean temperature and absolute humidity and (c) wind speed and air pollution index

Table 1 Descriptive statistics of COVID-19 cases and weather variables in Kuala Lumpur from 14 March 2020 to 31 August 2020

Parameters Meanplusmn SD Minimum Maximum aP (25) Median P (75) Mode bKut cAsydNDI 15plusmn 2530 0 204 2 7 20 ndash 177 36Temperature (degC) 295plusmn 113 2639 3167 2890 2972 306 30 03 ndash08Wind speed (ms) 156plusmn 047 072 304 116 152 188 103 07 10Absolute humidity (gm3) 227plusmn 106 1948 26 2180 2256 2350 2245 02 10eAPI 54plusmn 674 38 69 50 55 58 47 05 ndash02aPercental bKurtosiscAsymmetry dNew daily infection eAir pollution index


Table 2 Summary of Spearmanrsquos rank correlation test on the association of new daily COVID-19 cases and weather parameters in KualaLumpur from 14 March 2020 to 31 August 2020

NDI (degC) T (degC) u (ms) AH (gm3) APINew daily infection (NDI) 100Temperature (T) 038lowastlowast 100Wind speed (u) ndash036lowastlowast 012 100Absolute humidity (AH) 064lowastlowast 031lowastlowast ndash043lowastlowast 100Air pollution index (API) ndash035lowastlowast ndash001 029lowastlowast ndash036lowastlowast 100lowastlowastplt 001

10

05

00

ndash05

ndash10

log

RR

Mean temperature (degC)27 28 29 30

lag0ndash7

31

(a)

log

RR

Absolute humidity (gm3)

lag0ndash7

20 2221 23 24 25 26

05

00

ndash05

ndash10

(b)

10

05

00

ndash05

ndash10

log

RR

Mean temperature (degC)27 28 29 30 31

lag0ndash14

(c)

log

RR

Absolute humidity (gm3)20 2221 23 24 25 26

lag0ndash14

05

00

ndash05

ndash10

ndash15

(d)

10

05

00

ndash05

ndash10

log

RR


lag0ndash21

(e)

log

RR


lag0ndash21

05

00

ndash05

ndash10

ndash15

(f )

Figure 3 Continued


NDI above 226 gm3 was positive it was not statisticallysignificant in all the lags

4 Discussion

Previous studies have shown that temperature is an im-portant factor in the spread of human coronaviruses likeMERS-CoV [31] SARS-CoV [32 33] and COVID-19[11 12 15 19] We therefore compare our main findingswith the results of these studies In a laboratory study Chanet al [32] found that SARS virus could stay for 5 days on asmooth surface at temperature between 22 and 25degC with arapidly lost viability at higher temperatures of sim38degC Basedon data from Beijing Guangzhou Hong Kong and TaiyuanTan et al [33] also reported that the optimum environmentaltemperature for SARS virus was between 16 and 28degCSimilarly MERS-CoV has been reported to be highly un-stable at high temperature [31] Motivated by the significantdifferences in COVID-19 infections among different re-gions Bukhari and Jameel [17] directly compared the spreadof the virus and local environmental conditions globallyey concluded that mean temperatures between 3 and 10degCfavour the growth rate of the virus implying that highertemperatures could mitigate the infection expectedly so inwarmer humid climates Exploring the relationship betweenCOVID-19 case counts and meteorological parameters insome cities in China Liu et al [14] indicated that the

infection correlates negatively with temperature eyclaimed that every 1degC rise in air temperature reduces dailyinfections by a cumulative relative risk rate of 080 Bashiret al [12] however reported a weak but statistically sig-nificant positive correlation between COVID-19 new casesand mean temperature in New York City

Some of the studies cited above alluded to the conclusionthat while COVID-19 may thrive in some optimal tem-perature warmer temperature should suppress it In thisstudy however negative effect of high temperature onCOVID-19 transmission was not observed within the periodof evaluation A possible reason could be that the study areaexhibits higher temperature throughout the period of in-vestigation where the minimummean value was sim26degC witha maximum variation of 77degC Our result is howeverconsistent with similar association reported in Brazil [11]and Singapore [19] which have similar tropical climate Paniet al [19] explored the role of weather data on the trans-mission of COVID-19 in Singapore from 23 January to 31May 2020 and found it to be positively associated with meantemperature Also Auler et al [11] using principal com-ponent analysis found that soaring viral infection is asso-ciated with rising temperatures Moreover Xie and Zhu [15]explored the relationship between ambient temperature anddaily COVID-19 cases in 122 Chinese cities from 23 Januaryto 29 February 2020 and reported that 1degC rise in temper-ature is associated with a 4861 increase in the daily

Table 3 e effects of 1degC and 1 gm3 increases in mean temperature and absolute humidity on new daily COVID-19 infection

Lag Percent change in temperature () 95 CI Percent change in absolute humidity () 95 CITle 297degC AHle 226 gm3

lag0ndash7 1024lowast 0721ndash2864 2218lowast 1057ndash3946lag0ndash14 0891 0086ndash2004 2857lowast 1721ndash4078lag0ndash21 0783 0014ndash2164 2936lowast 1654ndash4709lag0ndash28 1462lowast 0191ndash3170 3807lowast 2064ndash5732

Tgt 297degC AHgt 226 gm3

lag0ndash7 2341lowast 1085ndash4140 0632 0410ndash1364lag0ndash14 2604lowast 1326ndash5647 0479 0173ndash1042lag0ndash21 2916lowast 1017ndash5264 0308 0118ndash0501lag0ndash28 3210lowast 1372ndash7976 0108 0084ndash0401lowastplt 005 CI is confidence interval for the change in parameters

10

05

00

ndash05

log

RR

ndash10

27 28 29Mean temperature (degC)

30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)

Figure 3e exposure-response curves for the effects of temperature and absolute humidity on new daily COVID-19 infectione x-axes are themean temperature and absolute humidity for 7-day 14-day 21-day and 28-day moving average e y-axes indicate relative risk ratio (RR)


confirmed cases However they reported a threshold valueof 3degC beyond which the rate of infection becomes statis-tically insignificant e association between temperatureand new daily infections in Kuala Lumpur contradicts thefindings of Yao et al [18] who reported no correlationbetween daily cases of COVID-19 and temperature in China

AH has also been reported to associate well with thetransmission of coronavirus disease [8 14 17] Bukhari andJameel [17] analyzed global data from 20 January to 19March 2020 and reported that COVID-19 transmission ismore prevalent in areas where AH ranges between 3 and 9 gm3 Similarly Gupter et al [8] using data from 1 January to9 April 2020 in the US reported a favorable range for thevirus to stabilize as 4ltAHlt 6 gm3 eir conclusionswhich implied that areas with high AH will experience lowinfections may not be applicable in tropical regions Moreso the very short period of these investigations has thelikelihood of impairing the reported findings In our casethe significant positive influence of AH on NDI was non-linear e relationship was linear until at 2260 gm3 when astatistically nonsignificant negative response was observede implication of the positive correlation between theweather indicators and newly confirmed cases in this studyindicates that COVID-19 could vanish due to public healthinterventions and not because of warmer weather epublic or governments could therefore not expect warmerclimates to suppress this novel virus as canvassed in someinstances

We are however not unmindful of some limitationsposed to our study e study considered essentially theassociation of some weather factors on the evolution ofCOVID-19 daily infection from 14 March 2020 to 31 August2020 in Kuala Lumpur a tropical city e number ofCOVID-19 cases in a regioncountrystatecity is howeverdependent on several other factors including testing ratepopulation rate global mobility governmental policiesambient air and surface interactions among others [17 34]which are too complex to aggregate in the current studyMore so the weather factors considered in our study onlyapply to outdoor transmission even though indoor anddirectindirect transmissions have also been reported eresult presented here is aimed at evaluating possible influ-ence of tropical weather variables on the viability of COVID-19 It is suggestive therefore that other variables such assurface pressure dewpoint temperature water vaporrainfall and horizontal wind speed could be included insubsequent evaluation of the spread of the virus in a tropicalclimate

5 Conclusion

Insufficient information on the novel coronavirus alsoknown as COVID-19 has made it more difficult for theworld to tackle its ongoing implosion In this study wepresented the possible association between daily com-munity infections and some meteorological parametersis was done by analyzing data from 14 March 2020 to 31August 2020 by considering new daily infections andlocation-specific temperature and absolute humidity over

Kuala Lumpur a tropical city Our results showed that newdaily COVID-19 confirmed cases correlate significantlywith temperature and absolute humidity in a nonlinearrelationship Each 1degC and 1 gm3 rise in mean tempera-ture and absolute humidity caused up to 3210 and3807 increases in the new daily COVID-19 infectionrespectively Within the study period mean temperatureand absolute humidity seemed to favor new daily infec-tions in Kuala Lumpur is finding is at variance withthose reported for temperate regions across the globewhere lower temperatures (3ndash17degC) and lower absolutehumidity (4ndash11 gm3) are said to enhance the growth rateof the disease [8 17]

Data Availability

Daily records of meteorological parameters were retrievedfrom the archives ofWeather Underground available online athttpswwwwundergroundcomweathermykuala-lumpurHourly air pollution index data based on mean concentrationsof PM10 O3 CO SO2 and NO2 were obtained from the airquality division of the Malaysian Department of Environmentat httpapimsdoegovmypublic_v2api_tablehtml Second-ary COVID-19 cases for the statescities were obtained fromthe official website of theMinistry ofHealthMalaysia at httpcovid-19mohgovmy

Conflicts of Interest

e authors declare that they have no conflicts of interest

Authorsrsquo Contributions

Ezekiel Kaura Makama contributed to data curation pro-vided software and wrote the original draft Hwee San Limcontributed to reviewing and editing funding acquisitionand project administration

Acknowledgments

e authors are grateful to the Malaysia Department ofEnvironment for providing the air pollution index data usedin this study is work was conducted with financialsupport from RUI grant Burning in the Southeast AsianMaritime Continent (1001PFIZIK8011079) and FRGSInvestigation of the Direct and Semidirect Radiative Effect ofBurning Aerosols from AERONETand MPLNET Data overSoutheast AsianMaritime Continent (203PFIZIK6711608)

Supplementary Materials

Secondary data on daily COVID-19 infections for KualaLumpur obtained from the official website of theMinistry ofHealth Malaysia (httpcovid-19mohgovmy) along withair pollution index observations are provided as supple-mentary data Daily mean temperature dewpoint windspeed and relative humidity have also been included(Supplementary Materials)


References

[1] C Huang Y Wang X Li et al ldquoClinical features of patientsinfected with 2019 novel coronavirus in Wuhan Chinardquo 6eLancet vol 395 pp 497ndash506 Article ID 10223 2020

[2] P Zhou X-L Yang X-G Wang et al ldquoA pneumoniaoutbreak associated with a new coronavirus of probable batoriginrdquo Nature vol 579 no 7798 pp 270ndash273 2020

[3] World Health Organization WHOEurope | CoronavirusDisease (COVID-19) Outbreak - WHO Announces COVID-19Outbreak a Pandemic World Health Organization GenevaSwitzerland 2020

[4] C-C Lai T-P Shih W-C Ko H-J Tang and P-R HsuehldquoSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19) the ep-idemic and the challengesrdquo International Journal of Anti-microbial Agents vol 55 no 3 Article ID 105924 2020

[5] World Health Organization ldquoCoronavirus disease 2019(COVID-19)rdquo Situation Report ndash 66 World Health Orga-nization Geneva Switzerland 2020

[6] Y Wang Y Wang Y Chen and Q Qin ldquoUnique epide-miological and clinical features of the emerging 2019 novelcoronavirus pneumonia (COVID-19) implicate special con-trol measuresrdquo Journal of Medical Virology vol 92 no 6pp 568ndash576 2020

[7] S S Gunthe B Swain S S Patra and A Amte ldquoGumpte2020pdfrdquo Journal of Public Health from 6eory to Practicalsvol 22 2020

[8] S Gupta G S Raghuwanshi and A Chanda ldquoEffect ofweather on COVID-19 spread in the US a prediction modelfor India in 2020rdquo Science of 6e Total Environment vol 728Article ID 138860 2020

[9] R Tosepu J Gunawan D S Effendy et al ldquoCorrelationbetween weather and covid-19 pandemic in Jakarta Indo-nesiardquo Science of the Total Environment vol 725 2020

[10] N L A Rani A Azid S I Khalit H Juahir andM S Samsudin ldquoAir pollution index trend analysis inMalaysia 2010-15rdquo Polish Journal of Environmental Studiesvol 27 pp 801ndash808 2018

[11] A C Auler F A M Cassaro V O Da Silva and L F PiresldquoEvidence that high temperatures and intermediate relativehumidity might favor the spread of COVID-19 in tropicalclimate a case study for the most affected Brazilian citiesrdquoScience of the Total Environment vol 729 2020

[12] M F Bashir and B Ma ldquoCorrelation between climate indi-cators and COVID-19 pandemic in New York USArdquo Scienceof 6e Total Environment vol 728 Article ID 138835 2020

[13] L M Casanova S Jeon W A Rutala D J Weber andM D Sobsey ldquoEffects of air temperature and relative hu-midity on coronavirus survival on surfacesrdquo Applied andEnvironmental Microbiology vol 76 no 9 pp 2712ndash27172010

[14] J Liu J Zhou J Yao et al ldquoImpact of meteorological factorson the COVID-19 transmission a multi-city study in ChinardquoScience of 6e Total Environment vol 726 Article ID 1385132020

[15] J Xie and Y Zhu ldquoAssociation between ambient temperatureand COVID-19 infection in 122 cities from Chinardquo Science of6e Total Environment vol 724 Article ID 138201 2020

[16] M Ahmadi A Sharifi S Dorosti S Jafarzadeh Ghoushchiand N Ghanbari ldquoInvestigation of effective climatology pa-rameters on COVID-19 outbreak in Iranrdquo Science of the TotalEnvironment vol 729 2020

[17] Q Bukhari and Y Jameel ldquoWill coronavirus pandemic di-minish by summerrdquo SSRN Electron Journal vol 66 2020

[18] Y Yao J Pan Z Liu et al ldquoNo association of COVID-19transmission with temperature or UV radiation in Chinesecitiesrdquo European Respiratory Journal vol 55 pp 7ndash92020

[19] S K Pani N-H Lin and S RavindraBabu ldquoAssociation ofCOVID-19 pandemic with meteorological parameters overSingaporerdquo Science of 6e Total Environment vol 740 ArticleID 140112 2020

[20] A Elengoe ldquoCOVID-19 outbreak in Malaysiardquo Osong PublicHealth and Research Perspectives vol 11 no 3 pp 93ndash1002020

[21] M Kumar M P Raju R K Singh A K Singh R S Singhand T Banerjee ldquoWintertime characteristics of aerosolsover middle Indo-Gangetic Plain vertical profile transportand radiative forcingrdquo Atmospheric Research vol 183pp 268ndash282 2017

[22] L W Vos H Leijnse A Overeem and R UijlenhoetldquoQuality control for crowdsourced personal weather stationsto enable operational rainfall monitoringrdquo Geophysical Re-search Letters vol 46 no 15 pp 8820ndash8829 2019

[23] K Zhang Y Li J D Schwartz and M S ONeill ldquoWhatweather variables are important in predicting heat-relatedmortality A new application of statistical learning methodsrdquoEnvironmental Research vol 132 pp 350ndash359 2014

[24] J Shaman and M Kohn ldquoAbsolute humidity modulates in-fluenza survival transmission and seasonalityrdquo Proceedingsof the National Academy of Sciences vol 106 no 9pp 3243ndash3248 2009

[25] S Li ldquoEnvironmentally mediated transmission models forinfluenza and the relationshipsrdquo 2011

[26] Y Ma Y Zhao J Liu et al ldquoEffects of temperature variationand humidity on the death of COVID-19 in Wuhan ChinardquoScience of 6e Total Environment vol 724 Article ID 1382262020

[27] R D Peng F Dominici and T A Louis ldquoModel choice intime series studies of air pollution and mortalityrdquo Journal ofthe Royal Statistical Society Series A (Statistics in Society)vol 169 no 2 pp 179ndash203 2006

[28] Q Zeng G Li Y Cui G Jiang and X Pan ldquoEstimatingtemperature-mortality exposure-response relationships andoptimum ambient temperature at the multi-city level ofChinardquo International Journal of Environmental Research andPublic Health vol 13 2016

[29] M Mofijur I M R Fattah A B M Saiful IslamS M A Rahman and M A Chowdhury ldquoRelationshipbetween climate variables and new daily COVID-19 cases inDhaka Bangladeshrdquo Sustainability vol 12 no 20 pp 1ndash112020

[30] D George and P Mallery SPSS for Windows Step by Step ASimple Guide and Reference 110 Update Allyn and BaconBoston MA USA 4th edition 2003

[31] N van Doremalen T Bushmaker and V J Munster ldquoSta-bility of Middle East respiratory syndrome coronavirus(MERS-CoV) under different environmental conditionsrdquoEurosurveillance vol 18 pp 1ndash4 2013

[32] K H Chan J S M Peiris S Y Lam L L M PoonK Y Yuen and W H Seto ldquoe effects of temperature andrelative humidity on the viability of the SARS coronavirusrdquoAdvances in Virology vol 2011 Article ID 734690 15 pages2011

[33] J Tan L Mu J Huang S Yu B Chen and J Yin ldquoAn initialinvestigation of the association between the SARS outbreak


and weather with the view of the environmental temperatureand its variationrdquo Journal of Epidemiology amp CommunityHealth vol 59 no 3 pp 186ndash192 2005

[34] L Patients D Taylor A C Lindsay and J P Halcox ldquoAerosoland surface stability of SARS-COV-2 as compared withSARS-COV-1rdquo 2020


Many studies have associated the spread of COVID-19with a wide range of factors among which are weath-er-related conditions [7ndash10] including environmental fac-tors such as air pollution index (API) [11 12] e survivaland transmission of the virus by droplets from infectedpersons are considered to be optimal in dry and cold weatherconditions [13] Studies investigating the effects of meteo-rological conditions on COVID-19 transmission since thebeginning of the year 2020 have largely been conducted inChina [14 15] Iran [16] Europe and the United States ofAmerica [17] ese reports and others have contradictoryconclusions on the relationship between meteorologicalfactors and COVID-19 transmission For instance Liu et al[14] based on data from 130 Chinese cities between 20January and 2 March 2020 concluded that lower temper-atures and humidity favor its transmission However Xieand Zhu [15] who investigated the relationship betweendaily mean temperature and newly confirmed COVID-19cases in many cities in China from 23 January to 29 Feb-ruary 2020 reported no evidence to suggest that case countswill decline in warm weather Yao et al [18] on the otherhand using the cumulative confirmed cases for 224 Chinesecities as of 9 March 2020 claimed the nonexistence ofcorrelation between COVID-19 and temperature or UVradiation However Bukhari and Jameel [17] analyzed thepatterns of local weather of the global regions affected byCOVID-19 and reported that most of the regions werewithin a certain range of temperature and absolute humidityis is corroborated by Gupta et al [8] who studied the effectof weather on COVID-19 in the United States (US) between1 January and 9 April 2020 Temperature and absolutehumidity have generally been reported as crucial weatherindices associated with the spread of COVID-19 eg [8 17]

Apart from the contradictory results on weath-er-COVID-19 relationship it can be argued that the re-ported association could differ in tropical climates whereboth temperature and humidity are almost uniformly highall year round with consequent low variations More so thestudies above covered very short periods of investigationwhich has the tendency to impair the outcome of theseinvestigations Higher rates of COVID-19 in countries likeIran Italy and South Korea and in some states in the USshare a similar temperature range of between 3 and 10degC[8 17] with Hubei in China the origin of the disease elower number of COVID-19 cases in Southeast Asiancountries (eg Malaysia Singapore and ailand) is at-tributed to warmer temperatures in the region [17] It couldtherefore be inferred that besides mobility and quaran-tining other factors are inimical to rising cases of COVID-19in a location

In this study the effects of location-specific weatherparameters on new daily COVID-19 confirmed cases inMalaysia are explored is is motivated by the significantdifferences in the growth rate of the disease found in dif-ferent countries and cities which is mostly attributed todifferent climatic features More so very few evaluationsbetween weather parameters and the evolution ofCOVID-19 exist for the tropical climate regions [9 11 19] Itis imperative therefore to investigate if tropical weather

parameters slow or aggravate the spread of the virus estudy specifically explores the association of temperature (T)and absolute humidity (AH) with new daily confirmedCOVID-19 infections (NDI) in Kuala Lumpure choice ofthe study area is informed by its tropical location highercases of the disease and its population density

2 Data and Methods

21 Study Area e first COVID-19 cases in Malaysia wererecorded on 25 January 2020 in Johor Bharu when someforeigners whose destination was traced back to China [20]were symptomatic e infection remained very low andlargely confined to imported cases not until the emergent oflocalized clusters which spiked due to a religious gatheringin Kuala Lumpur earlier in March 2020 e total number ofinfections in Malaysia from 14 March 2020 to 31 August2020 stood at 9353 with Kuala Lumpur Selangor NegeriSembilan Johor Sarawak and Sabah recording higher casesin that order Kuala Lumpur along with areas mentioned had7963 cumulative infections representing over 85 ofconfirmed cases in Malaysia within this period (seeFigure 1)

Kuala Lumpur the capital city of Malaysia is inwest-central Peninsular Malaysia on latitude 3141degN andlongitude 101687degE It is the countryrsquos largest urban areaand its cultural commercial and transportation centere climate is Af (tropical rainforest) under the Koppenclimate classification with high temperatures and hu-midity that vary little throughout the year e arithmeticmeans of maximum average and minimum temperaturesare respectively 35 29 and 234degC with an averagerelative humidity of 80 e area receives about2400mm of rain annually with June and July as the driestmonths Being a capital city Kuala Lumpur is the maindestination for investors job seekers and tourists It istherefore the most densely populated city in Malaysiawith about 8 million people residing within 243 km2which represents 314 annual growth rate (httpsworldpopulationreviewcomworld-citieskuala-lumpur-population) Analyzing the spread of COVID-19 trans-mission in relation to weather and environmental vari-ables in Kuala Lumpur is important because of itseconomic relevance and population density More soAuler et al [11] and Bashir et al [12] have emphasized onthe importance of evaluating weather parameters in adensely populated environment As of 31 August 2020 thecumulative COVID-19 confirmed infection in KualaLumpur stood at 2587 the highest in the country asshown earlier in Figure 1

22 Data Daily records of mean ambient temperature (degC)wind speed (ms) and relative humidity () for the period14 March to 31 August 2020 were retrieved from the ar-chives of Weather Underground available online at httpwwwwundergroundcom is popular and reliable onlineresource for meteorological data has been used extensivelyfor atmospheric research applications [7 19 21 22] AH



AH 21674H6112e

(1767TT+2435)

T + 27315 (1)






2i

n n2

minus 11113872 1113873 (2)




+ s(API k df + 1) + DoW(3)


8degN

6degN

4degN

2degN


150 75 0 150

Total COVID-19

N


2000ndash2500

gt2500300km

116degE 120degE













21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

1000

2000

3000

Cum

ulat

ive C

OV

ID-1

9

0

50

100

150

200

New

dai

ly in

fect

ion

(a)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

15

25

35

Tem

pera

ture

(degC)

and

abso

lute

hum

idity

(gm

3 )

(b)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

2

4

6

8

Win

d sp

eed

(ms

)

25

40

55

70

Air

pollu

tion

inde

x

(c)







10

05

00

ndash05

ndash10

log

RR


lag0ndash7

31

(a)

log

RR


lag0ndash7

20 2221 23 24 25 26

05

00

ndash05

ndash10

(b)

10

05

00

ndash05

ndash10

log

RR


lag0ndash14

(c)

log

RR


lag0ndash14

05

00

ndash05

ndash10

ndash15

(d)

10

05

00

ndash05

ndash10

log

RR


lag0ndash21

(e)

log

RR


lag0ndash21

05

00

ndash05

ndash10

ndash15

(f )

Figure 3 Continued



4 Discussion









10

05

00

ndash05

log

RR

ndash10


30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)






5 Conclusion



Data Availability






Acknowledgments





References







































AH 21674H6112e

(1767TT+2435)

T + 27315 (1)






2i

n n2

minus 11113872 1113873 (2)




+ s(API k df + 1) + DoW(3)


8degN

6degN

4degN

2degN


150 75 0 150

Total COVID-19

N


2000ndash2500

gt2500300km

116degE 120degE













21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

1000

2000

3000

Cum

ulat

ive C

OV

ID-1

9

0

50

100

150

200

New

dai

ly in

fect

ion

(a)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

15

25

35

Tem

pera

ture

(degC)

and

abso

lute

hum

idity

(gm

3 )

(b)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

2

4

6

8

Win

d sp

eed

(ms

)

25

40

55

70

Air

pollu

tion

inde

x

(c)







10

05

00

ndash05

ndash10

log

RR


lag0ndash7

31

(a)

log

RR


lag0ndash7

20 2221 23 24 25 26

05

00

ndash05

ndash10

(b)

10

05

00

ndash05

ndash10

log

RR


lag0ndash14

(c)

log

RR


lag0ndash14

05

00

ndash05

ndash10

ndash15

(d)

10

05

00

ndash05

ndash10

log

RR


lag0ndash21

(e)

log

RR


lag0ndash21

05

00

ndash05

ndash10

ndash15

(f )

Figure 3 Continued



4 Discussion









10

05

00

ndash05

log

RR

ndash10


30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)






5 Conclusion



Data Availability






Acknowledgments





References
















































21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

1000

2000

3000

Cum

ulat

ive C

OV

ID-1

9

0

50

100

150

200

New

dai

ly in

fect

ion

(a)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

15

25

35

Tem

pera

ture

(degC)

and

abso

lute

hum

idity

(gm

3 )

(b)


21-M

ar28

-Mar

04-A

pr11

-Apr

18-A

pr25

-Apr

02-M

ay09

-May

16-M

ay23

-May

30-M

ay06

-Jun

13-J

un20

-Jun

27-J

un04

-Jul

11-J

ul18

-Jul

25-J

ul01

-Aug

08-A

ug15

-Aug

22-A

ug29

-Aug

14-M

ar

0

2

4

6

8

Win

d sp

eed

(ms

)

25

40

55

70

Air

pollu

tion

inde

x

(c)







10

05

00

ndash05

ndash10

log

RR


lag0ndash7

31

(a)

log

RR


lag0ndash7

20 2221 23 24 25 26

05

00

ndash05

ndash10

(b)

10

05

00

ndash05

ndash10

log

RR


lag0ndash14

(c)

log

RR


lag0ndash14

05

00

ndash05

ndash10

ndash15

(d)

10

05

00

ndash05

ndash10

log

RR


lag0ndash21

(e)

log

RR


lag0ndash21

05

00

ndash05

ndash10

ndash15

(f )

Figure 3 Continued



4 Discussion









10

05

00

ndash05

log

RR

ndash10


30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)






5 Conclusion



Data Availability






Acknowledgments





References








































10

05

00

ndash05

ndash10

log

RR


lag0ndash7

31

(a)

log

RR


lag0ndash7

20 2221 23 24 25 26

05

00

ndash05

ndash10

(b)

10

05

00

ndash05

ndash10

log

RR


lag0ndash14

(c)

log

RR


lag0ndash14

05

00

ndash05

ndash10

ndash15

(d)

10

05

00

ndash05

ndash10

log

RR


lag0ndash21

(e)

log

RR


lag0ndash21

05

00

ndash05

ndash10

ndash15

(f )

Figure 3 Continued



4 Discussion









10

05

00

ndash05

log

RR

ndash10


30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)






5 Conclusion



Data Availability






Acknowledgments





References







































4 Discussion









10

05

00

ndash05

log

RR

ndash10


30 31

lag0ndash28

(g)

log

RR


lag0ndash28

05

00

ndash05

ndash10

ndash15

(h)






5 Conclusion



Data Availability






Acknowledgments





References









































5 Conclusion



Data Availability






Acknowledgments





References






































References






































effectsoflocation-specificmeteorologicalfactorsoncovid-19...

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