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

Area of Science:

• Environmental Science
• Epidemiology
• Public Health

Background:

• The COVID-19 pandemic caused by SARS-CoV-2 has seen uneven global distribution of cases and severity.
• Factors beyond initial infections, testing, and social behaviors may explain regional disparities in COVID-19 outcomes.
• Understanding transmission dynamics is crucial for managing outbreaks and projecting future trends.

Purpose of the Study:

• To identify factors contributing to the observed asymmetry in COVID-19 cases and severity across different regions.
• To develop a predictive tool supporting the design of effective containment policies for infectious disease outbreaks.
• To investigate the relationship between environmental factors, specifically air pollution, and SARS-CoV-2 transmission.

Main Methods:

• Development of a binary classifier utilizing an artificial neural network (ANN).
• Analysis of correlations between SARS-CoV-2 infection frequency and various air pollutants.
• Statistical examination of the relationship between particulate matter 2.5 (PM2.5) and ozone gas with COVID-19 case numbers.

Main Results:

• The artificial neural network model aids in explaining disparities in COVID-19 spread.
• A positive correlation was identified between SARS-CoV-2 infection frequency and exposure to particulate air pollutants, particularly PM2.5.
• Ozone gas showed a negative correlation with the number of infected individuals.

Conclusions:

• Atmospheric air pollutants, especially PM2.5, are linked to increased SARS-CoV-2 infection rates.
• Air pollutants can potentially serve as surrogate markers for anticipating infectious disease outbreaks.
• Findings support the integration of environmental monitoring into public health strategies for pandemic preparedness.