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

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 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.

COVID-19 transmission risk factors

“…However, the greater health-care density in urban areas [ 21 ] will also lead to a quicker identification and reporting of cases [ 20 ], which would also explain part of the dependence we observe. Reporting bias described in [ 20 ], together with the growth of the awareness of the pandemic (more intense use of social distances and masks in countries where the epidemic started later) over the period described in our work, is also at work for DATE (see also [ 46 ] for a study, subsequent to our work, that confirms similar conclusions).…”

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

“…Since the λ + value is directly related to the basic reproduction number R 0 , which characterizes the inherent biological transmission of the virus in an (initially) completely unprotected population (λ + and R 0 are roughly proportional, in realistic parameter ranges) [57], this conclusion has a simple and yet important interpretation. Namely, it means that medical, demographic, and environmental predispositions (determining R 0 ) [70,71] should be expected to strongly influence the behavior of the considered observables (detected cases and fatalities attributed to the virus) [72][73][74]. On the other hand, R 0 seems not to significantly impact the infection extinguishing time.…”

Modeling the COVID-19 transmission dynamics and identifying meteorological and sociodemographic predictors

“…First, we determined bivariate Pearson correlations of R0 with different meteorological and sociodemographic variables [1]. Then, we performed an advanced statistical analysis to infer the direct R0 predictors [2]. Namely, we applied the Principal Component Analysis to the meteorological and demographic variable sets to exchange them for the corresponding sets of non-correlated Principal Components (PCs).…”