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.

中文翻译：

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通过特征选择方法推断 SARS-CoV-2 全球传播的主要驱动因素

确定 SARS-CoV-2 在人群中传播的主要环境驱动因素对于了解 COVID-19 和其他传染病当前和未来潜在的爆发至关重要。为了解决这个问题，我们关注基本传染数R ₀，它对测试覆盖率不敏感，代表在没有社交距离和完全易感人群中的传播性。虽然许多变量可能会影响R ₀，但这些变量之间的高度相关性可能会模糊结果解释。因此，我们将主成分分析与几种基于回归的方法中的特征选择方法相结合，以确定R ₀背后的主要人口和气象驱动因素。我们稳健地得出，国家的财富/发展（人均 GDP 或人类发展指数）是全球层面最重要的R ₀预测因子，可能是人口总体接触频率的良好代理。这一主要影响受到人均建筑面积（​​室内空间的拥挤程度）、感染的发生（可能与感染风险意识的提高有关）、净迁移、不健康的生活方式/条件（包括污染、季节性和可能的​​卡介苗接种）的调节流行率。此外，我们认为与传递性显着相关的几个变量不会直接影响R ₀或对它的影响与朴素分析所建议的不同。