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Extended Data Text S1. Calculation of the basic reproduction number (R0) of SARS-
CoV-2 type S and G.
Once the herd immunity to type S is established, the proportion of the immunized
population (pS) will be 1 – 1/R0S. When x, y, and z proportions of the population are
exposed to type S, K, and G, respectively, the prevalence of infection after the exposure
to type K and G (IK and IG) is:
IK = pK y – pS x = (1 – 1/R0K) y – (1 – 1/R0S) x
IG = pG z – pK y = (1 – 1/R0G) z – (1 – 1/R0K) y
Since the prevalence is proportional to the PCR positive rate ([PCR]):
IK = α [PCR]K
IG = α [PCR]G
By substituting various epidemiological parameters, the constants in these equations can
be roughly determined. In Hiroshima, where a clear type S wave is seen (Fig. 1b) and
many Chinese tourists visit, the entire population is expected to be exposed to type S
and K (x = 1, y =1) and the PCR positive rate is 0.18% ([PCR]G = 0.0018):
IK = 1/R0S – 1/R0K = 0.0018α
The highest PCR positive rate has been reported from the Philippines ([PCR]G = 0.573
as of 3 April 2020).1 There would have been no previous exposure to either type K or G
(x = 0, y =0), and full herd immunity to type G is expected (z = 1):
IG = 1 – 1/R0G = 0.573α
The PCR positive rate in Hokkaido rose from 2.17% to 20.83% after the type G virus
epidemic (Extended Data Fig. 1a). Thus,
[PCR]G = 0.2083 – 0.0217 = 0.1866
with herd immunity to K and G viruses expected (y = 1, z = 1):
IK = pK y – pS x = 1 – 1/R0K – (1 – 1/R0S) x = 0.0217α
IG = 1/R0K – 1/R0G = 0.1866α
Solving these equations using R0K = 2.22 gives:
α = 1.41, xHokkaido = 0.948, R0S = 2.19, R0G = 5.23
pS = 1 – 1/R0S = 0.543
pK = 1 – 1/R0K = 0.545
pG = 1 – 1/R0G = 0.809
Extended Data Text S2. Exposure of Aichi and Fukuoka prefectures to type S, K,
and G viruses.
In Aichi (y = 1), RT-PCR positive rates rose from 0.94% (13 February 2020) to 10.06%
(6 April):
[PCR]K = 0.0094, [PCR]G = 0.1006
IK = (1 – 1/R0K) y – (1 – 1/R0S) x
= 0.545y – 0.543x = 0.0094α
IG = pG z – pK y
= (1 – 1/R0G) z – (1 – 1/R0K) y
= 0.809z – 0.545y = 0.0966α
Solving this equation gives:
xAichi = 0.980, zAichi = 0.754
In Fukuoka (y = 1):
[PCR]K = 0.0051 (16 March 2020)
IK = 0.545 – 0.543x = 0.0051α
Solving this equation gives:
xFukuoka = 0.991
Extended Data Text S3. Subcluster analysis.
We created a 2 x 2 table and estimated the number of affected individuals when the
proportion of foreigners to Japanese is the same across the population:
PCR (+) PCR (-)
Japanese a b a + b
Foreigner c d c + d
a + c b + d
c/a = 640/1007 = 0.636
The RT-PCR positive rate in the general population of Japan on 26 March 2020 was:
a/(a + b) = 1647/26401 = 0.0624
The approximate total population of Japanese is:
a + b = 126.8 x 106
Assuming that the proportion of foreigners in the total population is f:
f = (c + d)/ (a + b)
Solving these equations gives:
a = 7.91 x 106
b = 118.89 x 106
c = 5.03 x 106
d = 7.91 x (16.03f – 0. 636) x 106
Since d > 0,
f > 0.0397
c + d > 5.03 x 106
imposing constraints on these variables.
If f = 0.04:
d = 0.589 x 106
The prevalence of SARS-CoV-2 among foreigners is:
c/(c + d) = 0.749 (74.9%) (P = 0, McNemar's test)
If this subpopulation has established herd immunity:
R0 = 1/(1 – 0.749) = 3.99
Extended Data Text S4. CFR on exposure to type S, K, and G viruses.
The reduction in CFR (F) of the present type that had been caused by previous infection
by another type was defined as an attenuating factor (a). When calculated using the
prevalence (I) and the exposure to type S (x) , K (y), and G (z):
FK = F0K y – a(K-S) IS
= F0K y– a(K-S) (1 – 1/R0S) x
FG = F0G z – a(G-K) IK– a(G-S) IS
= F0G z – a(G-K) [(1 – 1/R0K) y – (1 – 1/R0S) x] – a(G-S) (1 – 1/R0S) x
= F0G z– a(G-K) (1 – 1/R0K) y + (a(G-K) – a(G-S)) (1 – 1/R0S) x
Again, we roughly determined the constants in these equations by substituting
epidemiological parameters. In the Philippines (x = 0, y = 0, z = 1):
FG = F0G z– a(G-K) IK – a(G-S) IS = F0G = 104/3764 = 4.70 (%) (7 April 2020)
In Hokkaido (y = 1, z = 1, IK = 0.0217α, xHokkaido = 0.948):
FG = F0G z – a(G-K) IK – a(G-S) (1 – 1/R0S) xHokkaido
= 4.70 – 0.0217α a(G-K) – 0.543a(G-S) xHokkaido = 4.19 (%) (7 April 2020)
In Aichi (y = 1, xAichi = 0.980, zAichi = 0.754, IK = 0.0094α, IG = 0.0966α, CFR = 8.81%
as of 6 April 2020)
FG = F0G z – a(G-K) IK – a(G-S) (1 – 1/R0S) xAichi
= 4.7zAichi – 0.0094α a(G-K) – 0.543a(G-S) xAichi = 8.81 (%)
Ehime Prefecture had the highest mortality rate before the appearance of the G
wave (Fig. 3b). The foreign tourist visit rate of Ehime is as low as 0.4%, ranking 39th
among 47 prefectures in Japan.3 Moreover, only 12.29% are Chinese. Ehime Prefecture
does not have any of the top 30 most popular tourist spots for foreign visitors to Japan.4
We therefore expect that type S virus failed to enter Ehime (x = 0):
FK = F0K – a(K-S) (1 – 1/R0S) x = F0K = 4.76 (%)
In Fukuoka (xFukuoka = 0.991, y = 1, CFR = 0.62%):
FK = F0Ky – a(K-S) IS = F0K – a(K-S) (1 – 1/R0S) xFukuoka = 4.76 – 0.538 a(K-S) = 0.62
These equations have been solved using α = 1.411:
a(K-S) = 7.69, a(G-K) = 313.43, a(G-S) = -17.66
FK = F0K y – a(K-S) (1 – 1/R0S) x
= 4.76y – 4.18x
FG = F0G z – a(G-K) (1 – 1/R0K) y + (a(G-K) – a(G-S)) (1 – 1/R0S) x
= 4.7z – 170.96y + 179.75x
Extended Data Text S5. Spread of type S, K, and G viruses in China.
In Wuhan (CFR = 2.9%):5
F = 4.7z – 166.20y + 175.58x = 2.9
Assuming that L type is mixture of type K and G, the frequency of L type and S type
was 96.3% and 3.7% in Wuhan, respectively:6
y + z = 0.963
x = 0.037
Assuming that three kinds of viruses were simultaneously epidemic, and residents were
exposed to any of the viruses:
x + y + z = 1.0
Solving the equation gives:
y = 0.0475, z = 0.9155
Outside Wuhan (CFR = 0.4%),5 the frequency of L and S types was 61.6% and 38.4%,
respectively:6
F = 4.7z – 166.20y + 175.58x = 0.4
y + z = 0.616
x = 0.384
Solving the equation gives:
y = 0.409, z = 0.207
References
1 Wikipedia. COVID-19 testing, <https://en.wikipedia.org/wiki/COVID-
19_testing> (2020).
2 Li, Q. et al. Early Transmission Dynamics in Wuhan, China, of Novel
Coronavirus-Infected Pneumonia. N Engl J Med 382, 1199-1207,
doi:10.1056/NEJMoa2001316 (2020).
3 Honichi Lab. [Ehime Prefecture Inbound Demand] (Japanese),
<https://honichi.com/areas/chugokushikoku/ehime/> (2020).
4 Honichi Lab. [Top 30 inbound tourist destinations] (Japanese). (2020).
5 Epidemiology Working Group for Ncip Epidemic Response, C. C. f. D. C. &
Prevention. [The epidemiological characteristics of an outbreak of 2019 novel
coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za
Zhi 41, 145-151, doi:10.3760/cma.j.issn.0254-6450.2020.02.003 (2020).
6 Lu, J. et al. On the origin and continuing evolution of SARS-CoV-2. National
Science Review, doi:10.1093/nsr/nwaa036 (2020).
Extended Data Figure legends Extended Data Fig. 1 | Changes in SARS-CoV-2 RT-PCR positive rate in Japan
prefectures. a, Curve of RT-PCR positive rate in prefectures where the outbreak of type
G (arrow) has ended. b, Prefectures that did not have type G trend until recently. c,
Prefectures where the type K epidemic (asterisk) is suppressing the next type G epidemic.
d, A map of prefectures which have both type K and G peaks (yellow-green), only type
K peaks (magenta), and no type K peak (red). e, Emergence of new RT-PCR positive
cases in Japan prefectures.
Extended Data Fig. 2 | Influenza epidemic curves in Japan prefectures.
Extended Data Fig. 3 | The risk scores and COVID-19 mortality in Japan. a,
Correlation between the risk score and case fatality rate (CFR) of COVID-19 in Japan
prefectures. Spearman correlation coefficient ρ = 0.365. b, Japan map showing the
distribution of risk scores of various strengths between prefectures.
Extended Data Fig. 4 | Phylogenetic tree of SARS-CoV-2 genome in the GISAID
database. a, Independent D614G mutation detected in Wuhan. b, ORF1b P314L
mutation detected in Italy. c, Outbreak of type G virus in Europe and the United States
and further spread to Asia. d, Division of SARS-CoV-2 into L type and S type by ORF8
gene allele.
Extended Data Fig. 5 | Influenza epidemic curves in European countries. Those with
SARS-CoV-2 CFR ≧ 1.0 are shown. Epidemic curves were created using influenza
surveillance data obtained from the World Health Organization (WHO).
Extended Data Fig. 6 | Influenza epidemic curves of European countries. Those with
SARS-CoV-2 CFR<1.0 are shown.
Extended Data Fig. 7 | Influenza epidemic curves of states in the United States.
Extended Data Fig. 8 | Influenza epidemic curves of states in the United States.
Extended Data Fig. 9 | Schematic representation of an epidemiological tool base on
influenza epidemic curve. A novel virus surveillance system that utilize competition
between viruses is proposed.
Kamikubo & Takahashi, Extended Data Fig. 1
a
b
Aichi Hokkaido
ChibaTokyo
c Tochigi Yamaguchi
d
e
* *
0
50
100
1 4 7 1013161922
Aichi
0
50
100
1 5 9 13 17 21
Hokkaido
0
5
10
15
20
1 4 7 1013161922
Yamanashi
0
20
40
60
1 5 9 13 17 21
Kyoto
0
50
100
1 4 7 1013161922
Tokyo
0
10
20
1 4 7 1013161922
Tochigi
0
20
40
60
1 5 9 13 17 21
Saitama
0
10
20
30
40
1 5 9 13 17 21
Gumma
0
20
40
60
1 5 9 131721
Chiba
0
50
100
150
1 5 9 13 17 21
Kanagawa
0
10
20
30
1 6 11 16 21 26
Niigata
0
10
20
30
40
1 4 7 1013161922
Nagano
0
10
20
30
1 4 7 10 13 16 19 22
Gifu
0
10
20
30
1 5 9 131721
Shizuoka
0
10
20
30
1 4 7 1013161922
Mie
0
5
10
15
1 4 7 1013161922
Nara
0
5
10
15
1 4 7 1013161922
Wakayama
0
20
40
60
1 5 9 13 17 21
Hyogo
0
50
100
1 4 7 1013161922
Osaka
0
5
10
15
1 4 7 1013161922
Kochi
0
10
20
30
40
1 4 7 1013161922
Kumamoto
0
5
10
15
1 5 9 13 17 21
Ehime
0
10
20
30
1 5 9 13 17 21 25 29
Miyazaki
0
10
20
30
1 5 9 13 17 21
Oita
0
10
20
30
1 4 7 1013161922
Nagasaki
0
20
40
60
1 4 7 10 13 16 19 22
Fukooka
0
20
40
60
1 4 7 1013161922
Hiroshima
0
10
20
30
1 4 7 1013161922
Ibaraki
Kamikubo & Takahashi, Extended Data Fig. 2
low risk
high risk
a
Kamikubo & Takahashi, Extended Data Fig. 3
CFR
(%)
Risk score
Aichi
HokkaidoHyogo
Osaka
Ehime
TokyoSaitama
Wakayama
KanagawaFukui
Toyama
IbarakiGifu
Fukuoka
a
b
c
d
Kamikubo & Takahashi, Extended Data Fig. 4
S type
L type
0
20
40
1 5 9 13 17 21
Ukraine
0
50
100
1 5 9 131721
Albania
0
100
200
1 5 9 13 17 21
Italy
0
10
20
30
1 5 9 13 17 21
Netherlands
0
2
4
1 4 7 1013161922
Greece
0
200
400
600
1 5 9 131721
UK
0
10
20
30
1 5 9 131721
Moldova
0
200
400
1 4 7 10 13 16 19 22
Turkey
0
50
100
1 4 7 1013161922
Bulgaria
0
200
400
600
1 4 7 1013161922
Spain
0
20
40
60
1 5 9 13 17 21
Hungary
0
100
200
1 5 9 13 17 21
France
0
20
40
1 5 9 131721
Belgium
0
20
40
60
1 5 9 13 17 21
Poland
0
50
100
150
1 5 9 13 17 21
Luxembourg
0
20
40
60
1 4 7 1013161922
Lithuania
0
50
100
1 5 9 13 17 21
Switzerland
0
20
40
60
1 4 7 1013161922
Sweden
Kamikubo & Takahashi, Extended Data Fig. 5
0
5
10
1 4 7 1013161922
Finland
0
50
100
150
1 5 9 13 17 21
Germany
0
10
20
30
1 4 7 1013161922
Romania
020406080
1 5 9 13 17 21
Slovenia
0
50
100
150
200
1 4 7 1013161922
Austria
02
46
8
1 4 7 1013161922
Montenegro
0
5
10
15
20
1 4 7 1013161922
Estonia
0
10
20
30
40
1 4 7 1013161922
Macedonia
0
10
20
30
1 4 7 10 13 16 19 22
Slovakia
0
5
10
15
20
1 4 7 1013161922
Belarus
0
10
20
30
1 4 7 101316192225
Czech Republic
0
5
10
15
20
1 4 7 10 13 16 19 22
Latvia
0
5
10
15
20
1 4 7 101316192225
Norway
0
20
40
60
1 4 7 1013161922
Portugal
0
50
100
150
1 5 9 13 17 21
Ireland
0
20
40
60
1 5 9 131721
Russia
0
50
100
1 4 7 1013161922
Serbia
0
10
20
30
1 5 9 13 17 21
Denmark
Kamikubo & Takahashi, Extended Data Fig. 6
0
5
10
15
1 6 111621
Alabama
0
5
10
15
1 6 11 16 21
Alaska
0
5
10
15
1 6 111621
Arizona
0
5
10
15
1 6 111621
Arkansas
0
5
10
15
1 5 9 131721
California
0
5
10
15
1 5 9 131721
Colorado
0
5
10
15
1 5 9 131721
Connecticut
02468
1 5 9 131721
Delaware
0
10
20
1 6 11 16 21
District of Columbia
0
5
10
15
1 6 11 16 21
Florida
0
5
10
15
1 6 111621
Georgia
0
5
10
15
1 6 11 16 21
Hawaii
0
2
4
6
1 5 9 13 17 21
Idaho
0
5
10
15
1 6 111621
Illinois
0
5
10
15
1 5 9 131721
Mississippi
0
5
10
15
1 5 9 131721
Indiana
0
10
20
1 6 111621
Iowa
0
5
10
15
1 6 111621
Kansas
0
10
20
1 5 9 131721
Kentucky
0
5
10
15
1 7 13 19
Louisiana
0
10
20
1 6 11 16 21
Maine
0
5
10
15
1 6 111621
Maryland
0
5
10
1 5 9 131721
Nevada
0
5
10
15
1 6 111621
New York
0
5
10
15
1 5 9 131721
New Hampshire
Kamikubo & Takahashi, Extended Data Fig. 7
0
5
10
15
1 5 9 131721
Tennessee
0
5
10
15
1 5 9 131721
Oregon
0
5
10
15
1 5 9 131721
Pennsylvania
0
5
10
15
1 5 9 131721
Rhode Island
0
5
10
15
1 5 9 131721
South Carolina
0
5
10
15
1 5 9 13 17 21
South Dakota
0
5
10
15
1 5 9 131721
Texas
0
5
10
15
1 5 9 13 17 21
Utah
0
5
10
15
1 5 9 131721
Vermont
0
5
10
15
1 5 9 131721
Virginia
0
5
10
15
1 5 9 13 17 21
Washington
0
10
20
1 5 9 131721
West Virginia
0
5
10
15
1 5 9 131721
Wisconsin
0
5
10
15
1 5 9 131721
Wyoming
Kamikubo & Takahashi, Extended Data Fig. 8
Influenza epidemics
2017 2018 2019 2020
Kamikubo & Takahashi, Extended Data Fig. 9