4.8 Article

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2019284118

Keywords

SARS-CoV-2; transmission; climate; seasonality; epidemiology

Funding

  1. Natural Environment Research Council [NE/V009710/1]
  2. Imperial College COVID-19 Research Fund
  3. NSF [ABI-1759965, EF1802605]
  4. NERC [NE/V009710/1] Funding Source: UKRI

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Research has shown that factors such as temperature, humidity, ultraviolet radiation, and population density can impact the transmission of SARS-CoV-2. Policy interventions and reductions in mobility are major predictors of COVID-19 transmission rates, while summer weather cannot substitute for mitigation policies.
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-CoV2) 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.

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