Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases

Area of Science:

• Epidemiology
• Environmental Science
• Data Science

Background:

• The COVID-19 pandemic highlighted the need for predictive tools to manage future outbreaks.
• Understanding factors influencing disease transmission is crucial for public health preparedness.

Purpose of the Study:

• To develop a model predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence.
• To evaluate the impact of meteorological, environmental, and policy variables on SARS-CoV-2 dynamics in Córdoba, Argentina.

Main Methods:

• A generalized additive model (GAM) was employed.
• Meteorological data (temperature), environmental factors (vegetation, PM2.5, NO2), and government restriction phases were analyzed.
• Lag effects and model performance metrics (R2adj, RMSE) were assessed.

Main Results:

• Mean temperature and vegetation cover were significant predictors of SARS-CoV-2 cases.
• Government restrictions, day of the week, and sunlight hours also influenced incidence.
• Particulate matter (PM2.5) and nitrogen dioxide (NO2) improved model prediction with a 1-day lag.

Conclusions:

• Data science offers valuable tools for identifying key variables in epidemiological patterns.
• Environmental factors and policy interventions significantly impact disease spread, informing public health management.
• Lockdown fatigue may contribute to increased cases despite policy interventions.