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

＊ ＊

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Miyazaki

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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

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Kamikubo & Takahashi, Extended Data Fig. 5

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Kamikubo & Takahashi, Extended Data Fig. 6

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Kamikubo & Takahashi, Extended Data Fig. 7

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Kamikubo & Takahashi, Extended Data Fig. 8

Influenza epidemics

2017 2018 2019 2020

Kamikubo & Takahashi, Extended Data Fig. 9