A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico
Abstract
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 (R0) 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.