Proc Natl Acad Sci U S A . Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions

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Proc Natl Acad Sci U S A


. 2021 Jun 22;118(25):e2019284118.
doi: 10.1073/pnas.2019284118.
Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions


Thomas P Smith ¹ , Seth Flaxman ² , Amanda S Gallinat ^{3 4} , Sylvia P Kinosian ^{3 4} , Michael Stemkovski ^{3 4} , H Juliette T Unwin ⁵ , Oliver J Watson ⁵ , Charles Whittaker ⁵ , Lorenzo Cattarino ⁵ , Ilaria Dorigatti ⁵ , Michael Tristem ⁶ , William D Pearse ^{1 3 4}



Affiliations

• PMID: 34103391
• DOI: 10.1073/pnas.2019284118

Abstract

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.

Keywords: SARS-CoV-2; climate; epidemiology; seasonality; transmission.