Weather drives variation in COVID-19 transmission and detection

Abstract

The debate over the influence of weather on COVID-19 epidemiological dynamics remains unsettled as multiple factors are conflated, including viral biology, transmission through social interaction, and the probability of disease detection. Here we distinguish the distinct dynamics of weather on detection and transmission with a multi-method approach combining econometric techniques with epidemiological models, including an extension of a susceptible-exposed-infectious-recovered model, to analyse data for over 4000 geographic units throughout the year 2020. We find distinct and significant effects of temperature, thermal comfort, solar radiation, and precipitation on the growth of infections. We also find that weather affects the rates of both disease transmission and detection. When we isolate transmission effects to understand the potential for seasonal shifts, the instantaneous effects of weather are small, with R0 about 0.007 higher in winter than in summer. However, the effects of weather compound over time, so that a region with a 5 ^∘ C drop over three months in winter is expected to have 190% more confirmed cases at the end of that 90 days period, relative to a scenario with constant temperature. We also find that the contribution of weather produces the largest effects in high-latitude countries. As the COVID-19 pandemic continues to evolve and risks becoming endemic, these seasonal dynamics may play a crucial role for health policy.

Keywords

• COVID-19
• temperature
• seasonal cycles
• Bayesian modeling
• transmission
• detection