Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

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

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.
  Article Metrics

Keywords COVID-19; epidemic model; Mexico; asymptomatic infection; basic reproduction number

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


  • 1. Cruz-Pacheco G, Bustamante-Castañeda JF, Caputo JG, et al. (2020) Dispersion of a new coronavirus SARS-CoV-2 by airlines in 2020: Temporal estimates of the outbreak in Mexico. Rev Invest Clin 72: 138-143.
  • 2. Vivanco-Lira A, (2020) Predicting COVID-19 distribution in Mexico through a discrete and timedependent Markov chain and an SIR-like model. arXiv 2003.06758.
  • 3. Alvarez MM, Gonzalez-Gonzalez E and Trujillo-de Santiago G, (2020) Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks. medRxiv.
  • 4. Acuña-Zegarra MA, Comas-García A, Hernández-Vargas E, et al. (2020) The SARS-CoV-2 epidemic outbreak: A review of plausible scenarios of containment and mitigation for Mexico. medRxiv.
  • 5. Acuña-Zegarra MA, Santana-Cibrian M and Velasco-Hernandez JX, (2020) Modeling behavioral change and COVID-19 containment in Mexico: A trade-off between lockdown and compliance. Math Biosci 325: 108370.    
  • 6. Lin Q, Zhao S, Gao D, et al. (2020) A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis 93: 211-216.    
  • 7. Khrapov P and Loginova A, (2020) Mathematical modelling of the dynamics of the Coronavirus COVID-19 epidemic development in China. Int J Open Inf Technol 8: 13-16.
  • 8. Fanelli D and Piazza F, (2020) Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals 134: 109761.    
  • 9. Caccavo D, (2020) Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. medRxiv.
  • 10. Singh R and Adhikari R, (2020) Age-structured impact of social distancing on the COVID-19 epidemic in India. arXiv 2003.12055.
  • 11. Bastos SB and Cajueiro DO, (2020) Modeling and forecasting the Covid-19 pandemic in Brazil. arXiv 2003.14288.
  • 12. Sharma S, Volpert V and Banerjee M, (2020) Extended SEIQR type model for COVID-19 epidemic and data analysis. medRxiv.
  • 13. Khoshnaw SHA, Shahzad M, Ali M, et al. (2020) A quantitative and qualitative analysis of the COVID-19 pandemic model. Chaos Solitons Fractals 138: 109932.    
  • 14. Monteiro LHA, (2020) An epidemiological model for SARS-CoV-2. Ecol Complex 43: 100836.    
  • 15. Nabi KN, (2020) Forecasting COVID-19 pandemic: A data-driven analysis. Chaos Solitons Fractals 139: 110046.    
  • 16. Okuonghae D and Omame A, (2020) Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria. Chaos Solitons Fractals 139: 110032.    
  • 17. Johns Hopkins CSSE, 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository, 2020. Available from: https://github.com/CSSEGISandData/COVID-19.
  • 18. Hethcote HW, (2000) The mathematics of infectious diseases. SIAM Rev 42: 599-653.    
  • 19. Diekmann O and Heesterbeek JAP, (2000) Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation, New York: John Wiley & Sons.
  • 20. Diekmann O, Heesterbeek JAP and Roberts MG, (2010) The construction of next-generation matrices for compartmental epidemic models. J Royal Soc Interface 7: 873-885.    
  • 21. Sjödin H, Wilder-Smith A, Osman S, et al. (2020) Only strict quarantine measures can curb the coronavirus disease (COVID-19) in Italy, 2020. Eurosurveillance 25: 2000280.
  • 22. Expansión política, La magnitud de la epidemia de COVID-19 no se puede medir en tiempo real, 2020. Available from: https://politica.expansion.mx/mexico.
  • 23. Tang B, Bragazzi NL, Li Q, et al. (2020) An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect Dis Model 5: 248-255.


This article has been cited by

  • 1. Bijay Kumar Sahoo, Balvinder Kaur Sapra, A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India, Chaos, Solitons & Fractals, 2020, 139, 110034, 10.1016/j.chaos.2020.110034

Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved