论文标题
2020年Covid-19大流行病的流行病学模型的基于伴随的数据同化
Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020
论文作者
论文摘要
数据同化用于最佳地拟合经典的流行病学模型与Covid-19大流行的Johns Hopkins数据。优化基于确认的病例和确认的死亡。这是唯一具有合理准确性的数据。可以从模型以及模型参数中推断出感染和恢复率。这些参数可以与政府行动或假期末期之类的事件联系起来。基于此数字，可以对未来进行预测，并指定目标。换句话说：我们为给定模型寻求解决方案，该模型以最佳意义适合给定数据。拥有该解决方案，我们都有所有参数。
Data assimilation is used to optimally fit a classical epidemiology model to the Johns Hopkins data of the Covid-19 pandemic. The optimisation is based on the confirmed cases and confirmed deaths. This is the only data available with reasonable accuracy. Infection and recovery rates can be infered from the model as well as the model parameters. The parameters can be linked with government actions or events like the end of the holiday season. Based on this numbers predictions for the future can be made and control targets specified. With other words: We look for a solution to a given model which fits the given data in an optimal sense. Having that solution, we have all parameters.
