论文标题
COVID-19传输的温度依赖性
Temperature dependence of COVID-19 transmission
论文作者
论文摘要
最近的冠状病毒大流行在其早期阶段几乎呈指数增长，许多国家 /地区在许多国家 /地区的案例数量非常适合$ n（t）\ propto e^{αt} $。我们分析了每个国家的利率$α$，从总体案例的阈值开始，然后使用接下来的12天，从而既同质地捕获早期增长。我们在流行病的月份中寻找$α$和每个国家的平均温度$ t $之间的联系。我们分析了42个国家的一组{\ it基地}集合，该{\ IT中间}集合了88个国家和{\ it扩展}集合的125个国家，该集已经发展了这一流行病。应用线性拟合$α（t）$，我们发现越来越多的证据表明$ t $的函数减少了$α$，$ 99.66 \％$ c.l。，$ 99.86 \％$ c.l。和$ 99.99995 \％$ c.l. （$ p $ -VALUE $ 5 \ CDOT 10^{ - 7} $，或5 $σ$检测）分别分别在{\ it base}中，{\ it InterMediate}和{\ it扩展}数据集。预计加倍时间将增加$ 40 \％\ sim 50 \％$，从$ 5^\ circ $ c到$ 25^\ circ $C。在{\ it base base}集合中，超越了线性模型，峰值为$（7.7 \ pm 3.6）^\ circ c $，但它的证据似乎是在较大的数据集中消失的。我们还分析了可能的偏见：贫穷的国家通常位于温暖的地区，可能的测试可能较低。通过排除在给定GDP以下的国家 /地区，我们发现我们的结论仅受到略有影响，并且仅对{\ it扩展}数据集有所影响。意义仍然很高，$ p $ - 价值为$ 10^{ - 3} -10^{ - 4} $或更少。我们的发现使人们希望，对于北半球国家，由于天气暖和和锁定政策，增长率应大大降低。通常，在寒冷季节到来之前，应有望通过强烈的锁定，测试和跟踪政策来阻止传播。
The recent coronavirus pandemic follows in its early stages an almost exponential growth, with the number of cases quite well fit in time by $N(t)\propto e^{αt}$, in many countries. We analyze the rate $α$ for each country, starting from a threshold of 30 total cases and using the next 12 days, capturing thus the early growth homogeneously. We look for a link between $α$ and the average temperature $T$ of each country, in the month of the epidemic growth. We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, an {\it intermediate} set of 88 countries and an {\it extended} set of 125 countries, which developed the epidemic more recently. Applying a linear fit $α(T)$, we find increasing evidence for a decreasing $α$ as a function of $T$, at $99.66\%$C.L., $99.86\%$C.L. and $99.99995 \%$ C.L. ($p$-value $5 \cdot 10^{-7}$, or 5$σ$ detection) in the {\it base}, {\it intermediate} and {\it extended} dataset, respectively. The doubling time is expected to increase by $40\%\sim 50\%$, going from $5^\circ$ C to $25^\circ$ C. In the {\it base} set, going beyond a linear model, a peak at $(7.7\pm 3.6)^\circ C$ seems to be present, but its evidence disappears for the larger datasets. We also analyzed a possible bias: poor countries, often located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita, we find that our conclusions are only slightly affected and only for the {\it extended} dataset. The significance remains high, with a $p$-value of $10^{-3}-10^{-4}$ 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 the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the cold season.
