COVID-19 puzzle in China: a serendipitous interplay between transmissibility and social distancing measures
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 non-homologous model, where social distancing effects are introduced analogous to course 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), which we explain through interplay of transmissibility and effective protection differences. That is, we obtain a negative feedback (also commonly encountered in systems biology) between transmissibility and effects of social distancing, with Hubei being an outlier with both large transmissibility and lower social distancing effects. While Case Fatality Rate is significantly larger for Hubei, we find that Infection Fatality Rates (IFRs) are much more uniform/consistent across all the provinces. Even for Wuhan, our inferred Infection Attack Rate is much below the herd immunity threshold, raising a major reemission risk. The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19, or other infectious diseases.