论文标题
数据驱动的分析和对墨西哥Covid-19的Seiard流行模型的预测
A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico
论文作者
论文摘要
我们提出了一个SEIARD数学模型，以研究墨西哥当前冠状病毒病（Covid-19）的爆发。我们对该模型进行了详细的分析，并使用公开报告的数据证明了其应用。我们通过下一代矩阵方法计算基本的繁殖编号（$ r_0 $），并估计每天感染，死亡和恢复率。 We calibrate the parameters of the SEIARD model to the reported data by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until June 2020. Our results estimate that the peak of the epidemic in Mexico will be around May 2, 2020. Our model incorporates the importance of considering the aysmptomatic infected individuals, because they represent the majority of the infected population (with symptoms or not) and they could play a huge role in在没有任何知识的情况下传播病毒。
We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. We conduct a detailed analysis of this model and demonstrate its application using publicly reported data. We calculate the basic reproduction number ($R_0$) via the next-generation matrix method, and we estimate the per day infection, death and recovery rates. We calibrate the parameters of the SEIARD model to the reported data by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until June 2020. Our results estimate that the peak of the epidemic in Mexico will be around May 2, 2020. Our model incorporates the importance of considering the aysmptomatic infected individuals, because they represent the majority of the infected population (with symptoms or not) and they could play a huge role in spreading the virus without any knowledge.
