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A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico

1 Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
2 Facultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tablaje Catastral 13615, C.P. 97119, Mérida, Yucatán, Mexico

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We propose an SEIARD mathematical model with different contact rates for the symptomatic and asymptomatic individuals 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 December 2020. Our model incorporates the importance of considering the asymptomatic 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.
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Citation: Ugo Avila-Ponce de León, Ángel G. C. Pérez, Eric Avila-Vales. A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico. Big Data and Information Analytics, 2020, 5(1): 14-28. doi: 10.3934/bdia.2020002

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