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Title

Trends in COVID-19 outbreak in Tokyo and Osaka from January 25 to May 06,

2020: a joinpoint regression analysis of the outbreak data

Authors

Syed Mahfuz Al Hasan1, Jennifer Saulam2, Kanae Kanda1, Nlandu Roger Ngatu1, and

Tomohiro Hirao1

Affiliations

1Department of Public Health, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe,

Miki-cho, Kagawa 761-0793, Japan

2Department of Food Processing and Nutrition, Karnataka State Akkamahadevi Women’s

University, Jnanashakti Campus, Torvi, Vijayapura 586108, Karnataka , India

Running title: COVID-19 outbreak in Tokyo and Osaka

Address correspondence to

Syed Mahfuz Al Hasan

Department of Public Health, Faculty of Medicine,

Kagawa University, Kagawa, Japan

1750-1 Ikenobe, Kita-gun

Miki-cho, Kagawa 761-0793, Japan

E-mail: mahfuz@med.kagawa-u.ac.jp

Tel: +81 87 891 2133

FAX: +81 87 891 2133

Key words: COVID-19, trend analysis, Tokyo, Osaka

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Summary

We analyzed the trends in the coronavirus disease 2019 (COVID-19) outbreak in Tokyo and

Osaka from January 25 to May 06, 2020. To analyze the trends and to identify significant

changes in trends we performed a joinpoint regression analysis. Since the last of March,

COVID-19 cases soared up in Tokyo (β = 103.51) and Osaka (β = 28.07). During the 2nd and

3rd weeks of April, both Tokyo (β = 157.53) and Osaka (β = 60.96) experienced the peak

growth in COVID-19 cases. Since then, with some hope, almost after 14 days of the state of

emergency declaration, the cases showed a containment trend but still increasing at a

concerning rate in Tokyo (β = 90.66).

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Tokyo, the capital as well as the political and economic center of Japan, and Osaka, the

economic hub of Japan, have been affected by the coronavirus disease 2019 (COVID-19)

pandemic since the last of January. With the emergence of this infectious disease, the

honorable Prime Minister Abe Shinzo has declared the state of emergency in Japan (1).

Tracing and monitoring continuously the trajectory and changes in the COVID-19 outbreak

(2) are very important to assess the taken initiatives to contain this pandemic and to have a

further decision. We provide the trends and significant changes in the coronavirus disease

2019 (COVID-19) outbreak in Tokyo and Osaka for more than 100 days, from January 25 to

May 06, 2020. Through our brief analysis, we identified some of the important points in time

where the outbreak trends changed significantly in these two important prefectures in Japan.

For this analysis, we used the number of confirmed COVID-19 cases data posted in the

official website of the Tokyo Metropolitan Government (3) and Osaka Prefectural

Government (4) about the new coronavirus outbreak in these two prefectures in Japan. We

compiled a comma-separated values (.csv) data file extracted from the graphs and tables

provided in these prefectural Government websites. To analyze the temporal trends and to

identify important changes in the trends in the COVID-19 outbreak, we performed a joinpoint

regression analysis (5). Joinpoint regression analysis is applied to trend analysis in cancer (5)

and non-cancer studies (6). With the joinpoint regression analysis, it is possible to identify

time when a meaningful change in the slope of a trend is detected over the study period. The

best fitting points, “called joinpoints,” are chosen when the slope changes significantly in the

joinpoint models. For this study, the joinpint models were selected based on the data-driven

Bayesian Information Criterion (BIC) method (7). Estimated regression coefficients (β) were

calculated for each of the trends extracted from the joinpoint models. Additionally, the

average daily percent change (ADPC), calculated as a geometric weighted average of the

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daily percent changes (8) since the state of emergency declaration date in Tokyo and Osaka

(April 07).

The number of cases increased by almost at the same rate in Tokyo (4.9% per day) and Osaka

(4.5% per day) since the Government has declared the state of emergency. With some hope,

after 14 days of the emergency, cases have been increasing at a contained rate in Tokyo

(2.4% per day) and more containment in Osaka (1.5% per day). Moreover, the daily new

cases in Tokyo also reduced by 3.1% per day since the state emergency declaration date.

Osaka showed more containment in daily new cases trend compared to Tokyo. Daily new

cases in Osaka reduced by 5.9% per day since the state of emergency declaration date and

reduced almost at a double rate (10.4% per day) since Apr 21.

Since Jan 25, COVID-19 cases in Tokyo was increasing at a slower rate (Table) until Mar 22

(β = 2.12; p < 0.001). From Mar 22, the cases started to increase at a concerning rate until the

end of March (β = 43.46; p < 0.001) and then soared up drastically (Figure A) more than at a

double rate until Apr 07 (β = 103.51; p < 0.001). From Mar 31 to Apr 07, within this one

week, the cases increased from 521 to 1194. Hereafter, with a more drastic situation, the

cases increased overwhelmingly in Tokyo by almost 158 cases/day until Apr 18. During this

probable peak growth period, COVID-19 cases in Tokyo reached to almost 3000. With some

hope, since Apr 18, the cases started to increase at a reduced rate (β = 125.53; p < 0.001) until

Apr 24 and since then it has been increasing at a more contained but still at a concerning rate

(β = 90.66; p < 0.001).

Rate of increase in COVID-19 cases in Osaka was lesser compared to Tokyo. Cases started to

increase since the last of February by almost 7 cases/day until Mar 28. Since Mar 28, cases in

Osaka soared up at a concerning rate until Apr 06 (β = 28.07; p < 0.001). Since then, almost

like Tokyo, Osaka experienced the peak growth since Apr 06 (β = 60.96; p < 0.001) until Apr

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21 (Table); though the number was one-third of Tokyo. Since Apr 21, the cases have been

increasing at a contained rate in Osaka (Figure B) and since Apr 30 the cases increased by

almost 13 cases/day. Moreover, since Apr 15 the daily new cases trend in Osaka showed a

downward slope (β = -2.99; p < 0.001).

In this short communication, we provided the trends and significant changes in the COVID-

19 outbreak in Tokyo and Osaka over 100 days, from Jan 25 to May 06, 2020. Since the last

week of March, cases soared up both in Tokyo and Osaka though there was a marked

difference in the number of cases between these two prefectures. Considering the 14 days

incubation period of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (9),

several situations might have caused the COVID-19 cases to soar up since the last of March.

Since the second week of March, infected travelers started to return from the new epicenters

of the SARS-CoV-2, mostly from Europe and American countries (10). With the increasing

cases trend and asymptomatic travelers from the epicenters, might have made difficult to

contain the spread of the SARS-CoV-2 in Tokyo and Osaka. Moreover, as the total number

of cases rises, the prefectural Government of Tokyo and Osaka have found it more and more

complicated to trace the routes of transmission and contain the unknown clusters (11).

The Japanese government has stated the state of emergency since Apr 07 (1). During the next

two weeks after the declaration, Tokyo and Osaka experienced the outbreak peak as cases

increased at the highest rate during this period (about 158 cases/day in Tokyo; about 61

cases/day in Osaka). But with some hope, since Apr 18 in Tokyo and since Apr 21 in Osaka,

we found that cases have been increasing at a contained rate in both of these prefectures.

Furthermore, in a more contained way, Since Apr 24, the cases have been increasing at a

further reduced but still concerning rate in Tokyo, about 91 cases/day. On the other hand,

Osaka showed a more contained growth in cases since the last of April. Considering the

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ongoing contained trend, the continuation of the state of emergency together with the other

initiative from the government (11) and cordial response from the people of Tokyo and Osaka

might work to further contain the spread of the SARS-CoV-2.

Conflict of interest

None to declare

Funding source

No funding source for this research

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References

1. Yuko A, Maiko E. Japan declares coronavirus state of emergency. Japan: NHK

WORLD-JAPAN News; 2020. Available from:

https://www3.nhk.or.jp/nhkworld/en/news/backstories/1014/

2. Al Hasan SM, Saulam J, Kanda K, Hirao T. The novel coronavirus disease (COVID-

19) outbreak trends in mainland China: a joinpoint regression analysis of the outbreak

data from January 10 to February 11, 2020 [preprint]. Bull World Heal Organ. 2020.

Available from: http://dx.doi.org/10.2471/BLT.20.251561

3. Tokyo Metropolitan Government. Latest updates on COVID-19 in Tokyo | Tokyo

Metropolitan Government COVID-19 Task Force website. Tokyo COVID-19

Information. 2020. Available from: https://stopcovid19.metro.tokyo.lg.jp/en/

4. Osaka Prefectural Government. Latest updates on COVID-19 in Osaka | 大阪府

新型コロナウイルス感染症対策サイト. COVID-19 Task Force website. 2020.

Available from: https://covid19-osaka.info/en

5. Kim HJ, Fay MP, Feuer EJ, et al. Permutation tests for joinpoint regression with

applications to cancer rates. Stat Med. 2000 ;19(3):335–51.

6. Al Hasan SM, Saulam J, Kanda K, Hirao T. Temporal Trends in Apparent Food

Consumption in Bangladesh: A Joinpoint Regression Analysis of FAO’s Food Balance

Sheet Data from 1961 to 2013. Nutrients. 2019;11(8):1864.

7. Zhang NR, Siegmund DO. A Modified Bayes Information Criterion with Applications

to the Analysis of Comparative Genomic Hybridization Data. Biometrics.

2007;63(1):22–32.

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8. Clegg LX, Hankey BF, Tiwari R, Feuer EJ, Edwards BK. Estimating average annual

per cent change in trend analysis. Stat Med. 2009;28(29):3670–82.

9. Yasinski E. Scientists Zero in on the Novel Coronavirus’s Incubation Period. The

Scientist. 2020 Jan 31.

10. Furuse Y, Ko YK, Saito M, Shobugawa Y, Jindai K, Saito T, et al. Epidemiology of

COVID-19 Outbreak in Japan, January-March 2020. Jpn J Infect Dis. 2020 (in press).

doi: 10.7883/yoken.JJID.2020.2020.271.

11. Ministry of Health Labour and Welfare. Analysis and Recommendations of the

Response to the Novel Coronavirus (COVID-19). Tokyo, Japan; 2020. Available from:

https://www.mhlw.go.jp/content/10900000/000620826.pdf

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Figure. Joinpoint regression analysis of the COVID-19 cases in Tokyo and Osaka from

January 25 to May 06, 2020. Joinpoint regression analysis of the number of cases in Tokyo

(A), cases in Osaka (B), daily new cases in Tokyo (C) and daily new cases in Osaka (D) from

January 25 to May 06, 2020. The vertical dotted lines represent the joinpoints. aDenotes the

number of confirmed cases infected with the SARS-CoV. bDenotes the number of confirmed

cases infected with the MERS-CoV. cDenotes the regression lines slope that is significantly

different from 0 for a specific trend (p < 0.05). Shaded area denotes the time since the state of

the emergency declaration in Japan.

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Table. Trends in the coronavirus disease 2019 (COVID-19) outbreak in Tokyo and Osaka from January 25 to May 06, 2020a

Trend 1 Trend 2 Trend 3 Trend 4 Trend 5 Trend 6

Period βb Period βb Period βb Period βb Period βb Period βb

Tokyo

Total cases Jan 25 –

Mar 22

2.12 Mar 22 –

Mar 31

43.46c Mar 31 –

Apr 07

103.51c Apr 07 – Apr

18

157.53c Apr 18 –

Apr 24

125.53c Apr 24 –

May 06

90.66c

Daily new

cases

Jan 25 –

Mar 23

0.14 Mar 23 –

Apr 11

8.32c Apr 11 –

May 06

-3.83c

Osaka

Total cases Jan 25 –

Feb 29

0.01 Feb 29 –

Mar 28

6.21c Mar 28 –

Apr 06

28.07c Apr 06 – Apr

21

60.96c Apr 21 –

Apr 30

31.49c Apr 30 –

May 06

12.66c

Daily new

cases

Jan 25 –

Mar 25

0.14 Mar 25 –

Apr 15

2.90c Apr 15 –

May 06

-2.99c

aTrends analysis identified joinpoints, which are points where line segment of trends are joined. Each joinpoint denotes a statistically significant change (p =

0.05) in the trend of the coronavirus disease 2019 (COVID-19) outbreak data in Tokyo and Osaka from January 25 to May 06, 2020.

bβ is the estimated regression coefficients for a specific trend; β was calculated from the data driven Bayesian Information Criterion (BIC) method for the

joinpoint model.

cDenotes that the regression lines slope is significantly different from 0 for a specific trend (p < 0.05).