CoVID-19、夏に減少、冬に次のピーク

1918年から流行した当時の新型インフルエンザH1N1スペイン風邪は2年間流行し、主に若い人を中心に5000万人程度を殺して、収束しました。

当時の人口20億人の30％程度が感染したと推測されています。

当時のH1N1は、現在のものより増殖が速く、肺胞炎を起こして.老人よりも若い人を殺しましたが、感染する対象が減ってくると、増殖速度を下げて、遠くまでひっそりと運んでもらい、感染者がすぐに死なないように弱毒化して現在の形になりました。

今回のCoVID-19は、インフルエンザより長い潜伏期を持ち、飛行機で遠くまで運んでもらうために一気に世界中に広がり、遅くとも人口の6割程度に感染したら、流行が収まると予測されています。

おそらく潜伏期が長い分、流行も長く、夏にやや流行が減速したり、秋になると再び大きな流行になりながら、数年間は流行が続くと思います。

終生免疫は付かないと言われていますが、それでも3年間程度は保持されるので、3年後にはかなり流行は小さくなっていることでしょう。

時々、Lockdownや休校を繰り返しながら、社会を持続するしかないという日々が数年間続くと思います。

手洗いと換気を習慣付ける必要があるでしょう。

（※以上は管理者文責）

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スウェーデン·Karolinska InstitutetのJan Albert氏らは、スイス·University of Baselとの共同研究で新型コロナウイルス（SARS-CoV-2）感染効率の季節性変動が今後の感染状況に与える影響を予測する数理モデルを作成。

ウイルス拡散が夏には縮小するものの秋冬に再び拡大する可能性を指摘した。詳細はSwiss Med Wkly（2020; 150: w20224）に掲載された。

＜ヒトコロナウイルス4種のデータでモデル化＞

Albert氏は「SARS-CoV-2拡散が夏に減少するとしても、パンデミックが終息したと結論づけることはできない。そうした縮小は一過性のものであり、感染コントロールの成果と季節性のウイルス拡散状況の変動による可能性があるためだ。

むしろ医療システムを整えワクチンと抗ウイルス薬の開発に投資する機会と捉えることができる」と述べている。

　普通感冒を引き起こすコロナウイルスの感染効率の季節性変動については、膨大なデータが存在する。

同氏らは、SARS-CoV-2が呼吸器感染を引き起こす近縁のコロナウイルス拡散と同じ季節性変動を示し、冬期に最も拡散が盛んになる傾向があると考えた。

そこで、近縁のコロナウイルス4種（HKU1、NL63、OC43、229E）の感染動態に関して、Karolinska University Hospitalの患者5万2,000例超のデータを収集。

感染症流行の数理モデル作成にしばしば用いられるSIRモデルに当てはめ、SARS-CoV-2の感染効率の季節性変動が北半球における同ウイルスの今後の感染状況に与える影響を予測した。

＜パンデミック評価では季節性の考慮を＞

"一般"コロナウイルス4種の感染（陽性率）は、7～9月の0.2％未満と比べ12～4月には約2％と10倍になっていた（図）。

単純SIRモデルに潜伏期間中の者を変数として追加したSIERモデルに、人口移動の影響をさらに加味し、北半球の温帯地域におけるSARS-CoV-2感染の季節性変動を解析した結果、2020年春に迎えたピークは夏には減少するが、2020/2021年冬には新たなピークを迎える可能性が想定された。

（Swiss Med Wkly 2020; 150: w20224）

Albert氏らは、ウイルス拡散にはさまざまな因子（例えば、実施された公衆衛生策の種類や隔離·検疫の成果など）が寄与しており、それらを全て考慮できなかったこと、解析に組み入れた変数についても不確実な点が多くあることから、今回の結果は「想定されるシナリオを検討するモデルにすぎない」と断った上で、「パンデミックに関するデータ評価では季節性が存在する可能性を念頭に置くことが重要」と結論づけている。

Swiss Med Wkly. 2020 Mar 16;150:w20224. doi: 10.4414/smw.2020.20224. eCollection 2020 Mar 9.

Potential impact of seasonal forcing on a SARS-CoV-2 pandemic.

Neher RA, Dyrdak R, Druelle V, Hodcroft EB, Albert J.

Abstract

A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 100,000 confirmed infections and 4000 fatalities (as of 10 March 2020). The outbreak has been declared a pandemic by the WHO on Mar 11, 2020. Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterise our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions. While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems.

PMID: 32176808 DOI: 10.4414/smw.2020.20224

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2020年2月の各都道府県の平均気温と2020年1月の中国からの旅行者の数について検証したところ、気温が低い地域ほど感染者数が多いという関連がみられました。

Effect of temperature on the infectivity of COVID-19

Mugen Ujiiea, Shinya Tsuzukib,*, Norio Ohmagaria

International Journal of Infectious Diseases 95 (2020) 301–303

Accepted 25 April 2020

https://www.ijidonline.com/article/S1201-9712(20)30284-8/pdf

Abstruct:

To evaluate the influence of temperature on the infectivity of COVID-19 in Japan.

Methods: We evaluated the relationship between the accumulated number of patients per 1,000,000 population and the average temperature in February 2020 in each prefecture by Poisson regression analysis. We introduced the monthly number of inbound visitors from China in January 2020 in each prefecture and old-age dependency ratio as additional explanatory variables in the model.

Results: Monthly inbound visitors from China in January 2020, old-age dependency ratio, and mean temperature in February 2020 are associated with the cumulative number of COVID-19 case on March 16, 2020. Conclusions: Our analysis showed a possible association between low temperature and increased risk of COVID-19 infection. Further evaluation would be desirable at a global level.

1. Introduction

The outbreak of novel coronavirus infection (COVID-2019) was first identified in Wuhan, China, in December 2019 (Zhu et al., 2020). A large number of cases have since occurred outside China, and the World Health Organization (WHO) declared the COVID-19 outbreak to be a pandemic in March 2020.

According to WHO situation report as of March 18, 2020 (World Health Organization, 2020), outside of China, Europe currently has registered the most cases to date, with other Asian countries registering a comparatively small number of cases.

Given the climatic differences between Europe and Asia, it seems reasonable to hypothesize that temperature is associated with the infectivity of COVID-19. This short report examines the relationship between temperature and the accumulated number of COVID-19 cases in Japan at the prefectural level.

2. Methods

Between January 15 and March 16, 2020, the Japanese Ministry of Health, Labour and Welfare reported 702 confirmed cases of COVID-19 across the country's 47 prefectures. Using data publicly available on the government's websites (Japan Meteorological Agency, 2020, Ministry of Health Labour and Welfare, 2020,

Ministry of Justice, 2020), we evaluated the relationship between the accumulated number of patients per 1,000,000 population and the average temperature in February 2020 in each prefecture by Poisson regression analysis. We introduced the monthly number of inbound visitors from China in January 2020 and old-age dependency ratio (the ratio of the number of people aged 65 and over, compared to the number of people 15-64 years old, derived from the website of Japanese government) in 2019 (Ministry of Internal Affairs and Communications, 2020) in each prefecture as additional explanatory variables in the model in order to reflect heterogeneity in force of infection from imported cases and age-structure of population. Statistical significance was defined by two-sided p-values < 0.05. Analysis was conducted by R, version 3.6.2 (R Core Team, 2018).

3. Results

Table 1 shows the cumulative number of COVID-19 cases per 1,000,000 population, the monthly inbound visitors from China in January 2020 per 1,000 population, and old-age dependency ratio. Table 2 shows the results of the Poisson regression analysis. All variables are associated with the cumulative number of COVID-19 case on March 16, 2020.

4. Discussion

To our knowledge, evidence of the association between temperature and number of COVID-19 cases has been scarce to date. However, Kissler and colleagues reported various projections of the current pandemics with considering the seasonality of its epidemiology, in addition to that other coronavirus strains have (Kissler et al., 2020). Albeit the seasonality in infectiousness of COVID-19 has not been empirically demonstrated yet, it would be plausible that COVID-19 shows higher infectivity in winter, like other betacoronaviruses. Considering this, our results suggest that low temperature might have positive impact on the infectivity of COVID-19.

Okinawa, the southernmost prefecture that sits in the subtropical zone, has reported only 3 cases so far. Conversely, Hokkaido, the northernmost prefecture that sits in the subarctic zone, has the largest number of reported cases in Japan. Their markedly different climates but use of the same surveillance system and same policy for PCR testing is advantageous for examining the hypothesis that temperature is associated with the infectivity of COVID-19, and helps address the difficulty in comparing the number of COVID-19 cases internationally because individual countries have their own testing policies. In light of this, we suggest that temperature could be a factor associated with the infectivity of COVID-19.

Frequency of social contact with infected people might be another risk factor for infection. From the viewpoint of social contact, we would expect there to be a higher risk of new cases in large cities., However, the largest number of COVID-19 cases in Japan were in Hokkaido, which has a much smaller population and population density than Tokyo. After we adjusted the model for

population and number of monthly inbound visitors from China in January 2020, lower temperature showed a strong correlation with a larger number of cumulative cases. In addition, we adjusted age- structure of population in each prefecture by introducing old-age dependency ratio, which showed correlation between low old-age dependency ratio and larger number of cases. This also supports our hypothesis because Hokkaido showed larger old-age depen- dency ratio than other large cities like Tokyo.

Our analysis has several limitations. First, we examined data solely from Japan, so it is not appropriate to generalize our results globally. Nevertheless, the diversity of climate yet uniform virus testing policy in Japan enabled us to examine the relationship between temperature and number of cases to some extent. Second, we could not include other confounding factors such as humidity in our analysis, which might have influenced our results.

In conclusion, our analysis showed a possible association between low temperature and increased risk of COVID-19 infection. Further evaluation would be desirable at a global level.

Ethical Approval

Not applicable because we used only published data and did not use any personal information.

References

Japan Meteorological Agency. Search of Past Weather Data; 2020. Available from: https://www.data.jma.go.jp/obd/stats/etrn/index.php. [Accessed March 18 2020].

Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 2020:eabb5793.

Ministry of Health Labour and Welfare. Current situation of novel coronavirus infections and response by the Ministry of Health, Labour and Welfare; 2020. Available from: https://www.mhlw.go.jp/stf/newpage_10226.html. [Accessed March 18 2020].

Ministry of Internal Affairs and Communications. Population, Population Dynamics and Number of Households Based on Basic Resident Register; 2020. Available from: https://www.soumu.go.jp/main_sosiki/jichi_gyousei/daityo/jinkou_jin- koudoutai-setaisuu.html. [Accessed April 21 2020].

Ministry of Justice. Immigration statistics; 2020. Available from: http://www.moj. go.jp/housei/toukei/toukei_ichiran_nyukan.html#a01.

R Core Team. R: A Language and Environment for Statistical Computing. Version 3.6.2 ed. Vienna, Austria: R Foundation for Statistical Computing; 2018.

World Health Organization. Coronavirus disease 2019 (COVID-19) Situation Report– 56. 2020.

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine 2020;382(8):727–33.

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ブラジル国内での気温差と新型コロナ患者数との関係を検討した研究では、25.8℃までは気温が下がるほど症例数が多くなるという結論になっています。

Science of The Total Environment

Volume 729, 10 August 2020, 138862

Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil

David N.Prata , WaldecyRodrigues , Paulo H.Bermejo

https://doi.org/10.1016/j.scitotenv.2020.138862

https://www.sciencedirect.com/science/article/pii/S0048969720323792?via%3Dihub