Temperature dependence of COVID-19 transmission.
Temperature dependence of COVID-19 transmission.
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DOI：
10.1016/j.scitotenv.2020.144390
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发表时间：
2021-04-01
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影响因子：
--
通讯作者：
Notari A
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文献类型：
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作者：
Notari A
最近的新冠肺炎大流行在早期阶段几乎是指数级的扩张，在许多国家，病例数量作为时间的函数与N(T)∝和eαt相当好地拟合。我们分析了不同国家的α率，每个国家从30个总病例的门槛开始，适合接下来的12天，从而以相当均匀的方式捕捉到早期的指数增长。我们寻找α率和每个国家在最初疫情增长月份的平均气温T之间的联系。我们分析了由42个国家组成的基本集合，这些国家在较早的阶段出现了疫情，中间集合包括88个国家，扩展的集合包括125个国家，它们最近出现了疫情。用线性行为α(T)拟合，我们发现在三个数据集中有越来越多的证据表明，在基础、中间和扩展数据集中，在高T下，分别在99.66%C.L.、99.86%C.L.和99.99995 C.L.(P值5⋅10−7，或5σ检测)下，扩散速度较慢。25摄氏度下的倍增时间比5摄氏度时长40%~150%。此外，我们分析了可能存在的偏差：通常位于温暖地区的贫穷国家可能没有那么激烈的测试。通过从数据集中排除低于给定人均GDP的国家，我们发现这只对我们的结论产生了轻微的影响，而且只对扩展数据集产生了影响。重要性始终很高，p值约为10−3-10−4或更低。我们的发现给了人们希望，对于北半球国家来说，由于天气变暖和封锁政策，增长率应该会大幅下降。一般而言，在下一个寒冷季节到来之前，应采取政策措施防止第二波，如公共建筑的安全通风、社会距离、使用口罩、测试和跟踪政策。
The recent COVID-19 pandemic follows in its early stages an almost exponential expansion, with the number of cases as a function of time reasonably well fit by N(t) ∝ eαt, in many countries. We analyze the rate α in different countries, starting in each country from a threshold of 30 total cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way. We look for a link between the rate α and the average temperature T of each country, in the month of the initial epidemic growth. We analyze a base set of 42 countries, which developed the epidemic at an earlier stage, an intermediate set of 88 countries and an extended set of 125 countries, which developed the epidemic more recently. Fitting with a linear behavior α(T), we find increasing evidence in the three datasets for a slower spread at high T, at 99.66% C.L., 99.86% C.L. and 99.99995% C.L. (p-value 5⋅10−7, or 5σ detection) in the base, intermediate and extended dataset, respectively. The doubling time at 25 °C is 40% ~ 50% longer than at 5 °C. Moreover we analyzed the possible existence of a bias: poor countries, typically located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita from the dataset, we find that this affects our conclusions only slightly and only for the extended dataset. The significance always remains high, with a p-value of about 10−3 - 10−4 or less. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general, policy measures should be taken to prevent a second wave, such as safe ventilation in public buildings, social distancing, use of masks, testing and tracking policies, before the arrival of the next cold season.
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