Spatial–Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak

  1. Raffaele Fronza
  2. Marina Lusic
  3. Manfred Schmidt
  4. Bojana Lucic

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Abstract

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation.

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    SciScore for 10.1101/2020.04.26.20080846: (What is this?)

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    SARS-CoV-2 is an emerging pathogen, and our study is with this regard subjected to possible limitations, primarily access to data and time constraints. Our model was validated based on publicly available data, different sampling policies and unknown fractions of untested/asymptomatic infected individuals, all of which could impact the accuracy of the collected datasets. Moreover, data access to the broader set of atmospheric factors with more powerful modeling strategies could be applied to establish explicit causal relation with the infection dynamic. Finally, the findings of our study should be seen in the light of the ongoing new pandemic, which makes our analysis short time windowed. On this point, temporal synchronization based on epidemiological parameters, was not implemented to synchronize the infection dynamics. Nevertheless, our data support the concept that the atmospheric conditions can both 1) promote the formation of persisting forms of airborne droplets charged with SARS-CoV-2 (PM2.5), and 2) reduce the activity of the virus (O3). We thus hypothesized that the increase of the concentration of PM2.5 may reflect the rise of infective droplets with a diameter inferior to 5 microns. We speculate that fast evaporation of droplets emitted by talking or sneezing increases the sustained circulation of micro-droplets that carry a higher viral load, particularly in closed spaces. In line with this, it has been shown for an airborne influenza virus that 49% of the viral p...

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  2. Version published to 10.3390/v12060588
  3. Version published to 10.1101/2020.04.26.20080846v1 on medRxiv