A systems biology approach to COVID-19 progression in population

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.

COVID-19 severity determinants inferred through ecological and epidemiological modeling

“…to account for contact tracing and hospitalization strategies [ 18 ], media effects [ 19 ], unreported cases [ 1 , 20 ], infected but asymptomatic individuals [ 21 ], uninfected but quarantined population [ 22 ], seasonality effects [ 23 ], etc. To extract the severity variable m / r directly from dynamical compartmental models, we used our SPEIRD modification [ 24 ] of the SEIR model, schematically represented in Fig. 1 .…”

“…The system of differential equations, which mathematically represents the model in Fig. 1 is given in [ 24 ]. From eqs.…”

BGRS/SB-2022 897 Combining machine learning and non-linear dynamics modeling to understand COVID-19 risk factors

“…Another difficulty is appropriately controlling the effects of multiple potentially important factors, due to both their mutual correlations, limited dataset, and potential non-linearities and interactions between these factors. To address these difficulties, we use epidemiological modeling to i) Estimate the basic reproduction number (R0) as a robust measure of the disease severity directly from observed infection count data [1], ii) Propose a disease severity measure independent of the disease transmission dynamics (i.e., the basic reproduction number) that has a direct mechanistic interpretation. The derived measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r) [2].…”

“…We use these two measures to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we use machine learning regressions that employ different feature selection methods [1][2][3]. To reduce dimensionality without complicating the variable interpretation and partially decorrelate the variable set, we employ Principal Component Analysis on subsets of mutually related (and highly correlated) predictors.…”

“…Finally, we investigate how the results change when the data are analyzed on a smaller special resolution (specifically, when going from USA states to USA counties). Results: We analyze COVID-19 transmissibility [1] and severity [2,3] on case count data from US states, where we assemble many potentially relevant variables. We use both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost).…”