A systems biology approach to COVID-19 progression in a population

TL;DR

This paper introduces a novel time-dependent epidemiological model inspired by systems biology to analyze COVID-19 progression, accounting for social distancing and regional differences, providing insights into infection dynamics across regions.

Contribution

The study develops a simple, adaptable model incorporating social distancing effects inspired by gene expression control, enabling analysis of regional COVID-19 data without overfitting.

Findings

Differences in transmissibility, protection, and detection explain regional infection disparities.

The model accurately captures regional COVID-19 outbreak dynamics.

Method can be applied globally to analyze COVID-19 and other infectious diseases.

Abstract

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.