COVID-19 severity determinants inferred through ecological and epidemiological modeling

TL;DR

This study introduces a novel epidemiological measure for COVID-19 severity that isolates disease severity from transmission dynamics, enabling identification of key demographic, medical, and environmental factors influencing severity.

Contribution

The paper proposes a new severity measure based on mortality and recovery rates, independent of transmission, and applies machine learning to identify known and novel severity predictors across US states.

Findings

Age, chronic diseases, and racial factors significantly influence severity.

Long-term pollution exposure and population density are identified as novel severity predictors.

The measure effectively isolates disease severity from transmission effects.

Abstract

Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Moreover, fatality counts (and related measures such as Case Fatality Rate) are dynamic quantities, as they appear with a delay to infections, while different geographic regions generally belong to different points on the epidemics curve. Instead, we use epidemiological modeling to propose a disease severity measure, which accounts for the underlying disease dynamics. The measure corresponds to the ratio of population averaged mortality and recovery rates (m/r). It is independent of the disease transmission dynamics (i.e., the basic reproduction number) and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate and multivariate analyses based on machine learning techniques, is used for selecting important predictors. Without using prior knowledge from clinical studies, we recover significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) predictors of the disease severity. Overall, the proposed measure is useful for inferring severity determinants of COVID-19 and other infectious diseases, and the obtained results may aid a better understanding of COVID-19 risks.