ABSTRACT

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.

摘要

我们调查了全球 228 个城市跨越三个气候区的气候对 COVID-19 传播风险的影响。基于 Boosted Regression Tree 算法方法的应用结果表明，平均温度和平均相对湿度解释了 COVID-19 在温带和亚热带地区传播的显着变化，而在热带地区，平均昼夜温度范围和温度季节性显着预测感染爆发。在法国、土耳其、美国、英国和德国的城市，阳性病例的数量急剧下降，高于 10°C 的平均温度。在热带国家中，印度城市的 COVID-19 受日平均温度的影响最大，而巴西城市受温度季节性的影响最大。