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An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast

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  • Avila-Ponce de León, Ugo
  • Pérez, Ángel G.C.
  • Avila-Vales, Eric

Abstract

We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R0) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R0. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.

Suggested Citation

  • Avila-Ponce de León, Ugo & Pérez, Ángel G.C. & Avila-Vales, Eric, 2020. "An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305610
    DOI: 10.1016/j.chaos.2020.110165
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    References listed on IDEAS

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    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    3. Pai, Chintamani & Bhaskar, Ankush & Rawoot, Vaibhav, 2020. "Investigating the dynamics of COVID-19 pandemic in India under lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Annas, Suwardi & Isbar Pratama, Muh. & Rifandi, Muh. & Sanusi, Wahidah & Side, Syafruddin, 2020. "Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. José M. Garrido & David Martínez-Rodríguez & Fernando Rodríguez-Serrano & Sorina-M. Sferle & Rafael-J. Villanueva, 2021. "Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months," Mathematics, MDPI, vol. 9(10), pages 1-21, May.
    2. Aguilar-Canto, Fernando Javier & de León, Ugo Avila-Ponce & Avila-Vales, Eric, 2022. "Sensitivity theorems of a model of multiple imperfect vaccines for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    4. Xiaofeng Liu & Zubair Ahmad & Ahmed M Gemeay & Alanazi Talal Abdulrahman & E H Hafez & N Khalil, 2021. "Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-31, July.
    5. Mario A Quiroz-Juárez & Armando Torres-Gómez & Irma Hoyo-Ulloa & Roberto de J León-Montiel & Alfred B U’Ren, 2021. "Identification of high-risk COVID-19 patients using machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    6. de León, Ugo Avila-Ponce & Avila-Vales, Eric & Huang, Kuan-lin, 2022. "Modeling COVID-19 dynamic using a two-strain model with vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    7. Basnarkov, Lasko & Tomovski, Igor & Sandev, Trifce & Kocarev, Ljupco, 2022. "Non-Markovian SIR epidemic spreading model of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Prem Kumar, R. & Santra, P.K. & Mahapatra, G.S., 2023. "Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 741-766.
    9. Vashishth, Anil K. & Basaiti, Komal, 2024. "Modeling the effect of non-pharmaceutical measures and vaccination on the spread of two variants of COVID-19 in India," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 139-168.

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