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[Preprint]. 2020 Mar 12:3552677.
doi: 10.2139/ssrn.3552677.

The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales

Canelle Poirier  1   2 , Wei Luo  1   2 , Maimuna S Majumder  1   2 , Dianbo Liu  1   2 , Kenneth D Mandl  1   2   3 , Todd A Mooring  4 , Mauricio Santillana  1   2   5
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The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales

Canelle Poirier et al. SSRN. .

Update in

Abstract

A novel coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 240,000 cases of COVID-19 worldwide as of March 19, 2020. Previous studies have supported an epidemiological hypothesis that cold and dry environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid environments see attenuated viral transmission (e.g., influenza). However, the role of temperature and humidity in transmission of COVID-19 has not yet been established. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case count without the implementation of extensive public health interventions.

Keywords: COVID-19; coronavirus Wuhan; digital epidemiology; machine learning; machine learning in public health; modeling disease outbreaks; precision public health.

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Conflict of interest statement

Competing Interest Statement

The authors have declared no competing interest.

Figures

Figure 1.
Figure 1.
Estimated reproductive numbers Rproxy by province plotted as a function of absolute humidity or temperature for both time periods. 87% confidence intervals are displayed as vertical lines and were obtained from the collection of Rproxy calculated in subsequent time windows of length d for each location.
Figure 2.
Figure 2.
Temperature in each provincial capital vs. COVID-19 Rproxy estimate (calculated for the first time period). The size and color of each pin indicate cumulative cases per province and Rproxy range, respectively.
See this image and copyright information in PMC

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