  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.
