Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.
Department of Biostatistics, University of California, Davis, CA, USA.
J Biol Dyn. 2022 Dec;16(1):412-438. doi: 10.1080/17513758.2022.2078899.
We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.
我们为美国的 COVID-19 病例和死亡数据拟合了一个 SARS-CoV-2 模型。我们的结论是该模型在结构上是不可识别的。我们通过将一些参数从前置外部信息中获得来使模型具有可识别性。通过蒙特卡罗模拟对模型进行实际可识别性的研究表明，其中两个参数可能无法进行实际识别。利用已识别的参数，我们建立了一个包含社交距离和隔离作为控制变量的最优控制问题。我们研究了两种情况：控制措施在整个时间段内应用和控制措施仅在时间段内应用。我们的结果表明，如果在疫情早期应用控制措施，与延迟 2 周应用控制措施相比，受感染人群的减少至少高出一个数量级。此外，在大流行结束之前取消控制措施会导致受感染人群的反弹。
