Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions
Edited by Nils Chr. Stenseth, Universitetet i Oslo, Oslo, Norway, and approved May 8, 2021 (received for review September 12, 2020)
Significance
There is still much to be understood about the factors influencing the ecology and epidemiology of COVID-19. In particular, whether environmental variation is likely to drive seasonal changes in SARS-CoV-2 transmission dynamics is largely unknown. We investigate the effects of the environment on SARS-CoV-2 transmission rates across the United States and then incorporate the most important environmental parameters into an epidemiological model. We show that temperature and population density can be important factors in transmission but only in the absence of mobility-restricting policy measures, although particularly strong policy measures may be required to mitigate the highest population densities. Our findings improve our understanding of the drivers of COVID-19 transmission and highlight areas in which policy decisions can be proactive.
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.
Data Availability
No new data are released as part of this paper, all external datasets used are publicly available, described in Materials and Methods and referenced. Outputs from our Bayesian model runs are available on Figshare (https://doi.org/10.6084/m9.figshare.14696841.v1) and code to reproduce our analyses are available on Zenodo (https://doi.org/10.5281/zenodo.4884696). Code to download and combine external datasets and reproduce our full analysis pipeline is also available in our team’s GitHub repository (https://www.github.com/pearselab/tyrell).
Acknowledgments
This work was funded by Natural Environment Research Council Grant NE/V009710/1 and by the Imperial College COVID-19 Research Fund. W.D.P. and the W.D.P. laboratory are also funded by NSF Grants ABI-1759965 and EF-1802605.
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Copyright © 2021 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Data Availability
No new data are released as part of this paper, all external datasets used are publicly available, described in Materials and Methods and referenced. Outputs from our Bayesian model runs are available on Figshare (https://doi.org/10.6084/m9.figshare.14696841.v1) and code to reproduce our analyses are available on Zenodo (https://doi.org/10.5281/zenodo.4884696). Code to download and combine external datasets and reproduce our full analysis pipeline is also available in our team’s GitHub repository (https://www.github.com/pearselab/tyrell).
Submission history
Published online: June 8, 2021
Published in issue: June 22, 2021
Keywords
Acknowledgments
This work was funded by Natural Environment Research Council Grant NE/V009710/1 and by the Imperial College COVID-19 Research Fund. W.D.P. and the W.D.P. laboratory are also funded by NSF Grants ABI-1759965 and EF-1802605.
Notes
This article is a PNAS Direct Submission.
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The authors declare no competing interest.
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