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 protectiondifferences. 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.
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.
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time-dependent
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course grained
coarse-grained
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), which we explain through interplay of transmissibility and effective protectiondifferences. 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
. 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
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the data (publicly available for a number of countries),
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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