Inferring the Main Drivers of SARS‐CoV‐2 Global Transmissibility by Feature Selection Methods

Abstract

Abstract Identifying the main environmental drivers of SARS‐CoV‐2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID‐19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression‐based approaches to identify the main demographic and meteorological drivers behind R0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built‐up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R0 or affect it differently than suggested by naïve analysis.

Keywords

• COVID‐19 environmental dependence
• disease spread risk factors
• basic reproduction number
• principal component analysis
• regression analysis
• feature selection