Abstract
The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown, as of April 2020, whether summer weather will reduce its spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies and research on related viruses predicted that COVID-19 would decline with higher temperatures, humidity, and ultraviolet (UV) light. Using current, fine-scaled weather data and global reports of infections, we develop a model that explains 36% of the variation in maximum COVID-19 growth rates based on weather and demography (17%) and country-specific effects (19%). UV light is most strongly associated with lower COVID-19 growth. Projections suggest that, without intervention, COVID-19 will decrease temporarily during summer, rebound by autumn, and peak next winter. Validation based on data from May and June 2020 confirms the generality of the climate signal detected. However, uncertainty remains high, and the probability of weekly doubling rates remains >20% throughout summer in the absence of social interventions. Consequently, aggressive interventions will likely be needed despite seasonal trends.
Highlights
The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives
We developed several predictions about how weather, either directly or indirectly via modified human behaviors or effects on immune function, affects COVID-19 growth rate based on a literature review of weather impacts on SARS-CoV-2 [9, 10, 12], related coronaviruses [8, 13,14,15], and viruses involved in other epidemics such as influenza [16,17,18,19]
We modeled maximum growth rates of COVID-19 cases to restrict analyses to the early growth phase before social interventions reduced transmission, but after community transmission began, and when most people were still susceptible to this novel virus
Summary
The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. Given the uncertainty in the joint distributions of symptom onset, testing, and reporting, as well as not knowing the degree to which variables influenced COVID-19 case growth via transmission versus the expression of symptoms (e.g., vitamin D immune function), we chose to average across the potential period of infectivity, thereby assuming weather each day in the preceding 14 d was important.
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