The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales

Abstract

First identified in Wuhan, China, in December 2019, a novel coronavirus (SARS-CoV-2) has affected over 16,800,000 people worldwide as of July 29, 2020 and was declared a pandemic by the World Health Organization on March 11, 2020. Influenza studies have shown that influenza viruses survive longer on surfaces or in droplets in cold and dry air, thus increasing the likelihood of subsequent transmission. A similar hypothesis has been postulated for the transmission of COVID-19, the disease caused by SARS-CoV-2. It is important to propose methodologies to understand the effects of environmental factors on this ongoing outbreak to support decision-making pertaining to disease control. 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 (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 counts without the implementation of drastic public health interventions.

Figure 1

Visualization of the relationship between COVID-19 transmission as captured by R_proxy and temperature and humidity. The data points on the scatter plot represent the value of Rproxy (with its associated 87% confidence intervals displayed as vertical lines, obtained from the collection of R_proxy calculated in subsequent time windows of length d for each location) as a function of temperature and humidity. The black line corresponds to a Loess regression aimed at capturing the relationship between Rproxyand temperature and humidity. In addition, the color intensity (orange) of each data point shows the size of the outbreak in each location, as captured by the log of cumulative case counts.

Figure 2

Temperature in each provincial capital vs. COVID-19 R_proxy estimate (calculated for the first time period). The size and color of each pin indicate cumulative cases per province and R_proxy range, respectively. (Map obtained with ArcMap, https://desktop.arcgis.com/en/arcmap/ version 10.2).

Figure 3

Absolute humidity in each provincial capital vs. R_proxy estimate (calculated for the first time period). The size and color of each pin indicate cumulative cases per province and R_proxy range, respectively. (Map obtained with ArcMap, https://desktop.arcgis.com/en/arcmap/ version 10.2).