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A number of models in mathematical epidemiology have been developed toaccount for control measures such as vaccination or quarantine. However,COVID-19 has brought unprecedented social distancing measures, with a challengeon how to include these in a manner that can explain the data but avoidoverfitting in parameter inference. We here develop a simple time-dependentmodel, where social distancing effects are introduced analogous tocoarse-grained models of gene expression control in systems biology. We applyour approach to understand drastic differences in COVID-19 infection andfatality counts, observed between Hubei (Wuhan) and other Mainland Chinaprovinces. We find that these unintuitive data may be explained through aninterplay of differences in transmissibility, effective protection, anddetection efficiencies between Hubei and other provinces. More generally, ourresults demonstrate that regional differences may drastically shape infectionoutbursts. The obtained results demonstrate the applicability of our developedmethod to extract key infection parameters directly from publically availabledata so that it can be globally applied to outbreaks of COVID-19 in a number ofcountries. Overall, we show that applications of uncommon strategies, such asmethods and approaches from molecular systems biology research to mathematicalepidemiology, may significantly advance our understanding of COVID-19 and otherinfectious diseases.

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