Drastic difference in the COVID-19  infection and fatality counts , observed between Hubei (Wuhan) and other Mainland China provinces raised public controversies. To address if these data can be consistently understood, we develop a model that takes into account all main qualitative features of the infection progression under suppression measures, while remaining simple enough to estimate the key dynamics parameters. We find that social distancing measures had a large effect in the provinces, where moreover higher transmissibility appears to have triggered more effective protection . Hubei is, however, a far outlier with significantly larger transmissibility , but lower protection. Predicted infection attack rate is significantly higher for Hubei, but much lower than necessary for herd immunity. Case Fatality Rate is five times larger in Hubei, but our estimated Infection Fatality Rates  are much more uniform/consistent across all provinces. Therefore, interplay of transmissibility, effective protection, and detection efficiency differences may explain apparently drastic count discrepancies .
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 .
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A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However,
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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
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raised public controversies. To address if these data can be consistently understood, we develop a model that takes into account all main qualitative features of the infection progression under suppression measures, while remaining simple enough to estimate the key dynamics parameters. We find that social distancing measures had a large effect in the provinces, where moreover higher transmissibility appears to have triggered more effective protection . Hubei is, however, a far outlier with significantly larger transmissibility , but lower protection. Predicted infection attack rate is significantly higher for Hubei, but much lower than necessary for herd immunity.
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provinces. Therefore, interplay of transmissibility, effective protection, and detection efficiency differences may explain apparently drastic count discrepancies
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