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Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion

Author

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  • Victoria Chebotaeva

    (Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA)

  • Paula A. Vasquez

    (Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA)

Abstract

We explore the effects of cross-diffusion dynamics in epidemiological models. Using reaction–diffusion models of infectious disease, we explicitly consider situations where an individual in a category will move according to the concentration of individuals in other categories. Namely, we model susceptible individuals moving away from infected and infectious individuals. Here, we show that including these cross-diffusion dynamics results in a delay in the onset of an epidemic and an increase in the total number of infectious individuals. This representation provides more realistic spatiotemporal dynamics of the disease classes in an Erlang S E I R model and allows us to study how spatial mobility, due to social behavior, can affect the spread of an epidemic. We found that tailored control measures, such as targeted testing, contact tracing, and isolation of infected individuals, can be more effective in mitigating the spread of infectious diseases while minimizing the negative impact on society and the economy.

Suggested Citation

  • Victoria Chebotaeva & Paula A. Vasquez, 2023. "Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2167-:d:1139648
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    References listed on IDEAS

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    4. Yongli Cai & Dongxuan Chi & Wenbin Liu & Weiming Wang, 2013. "Stationary Patterns of a Cross-Diffusion Epidemic Model," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-10, November.
    5. Alexandros Leontitsis & Abiola Senok & Alawi Alsheikh-Ali & Younus Al Nasser & Tom Loney & Aamena Alshamsi, 2021. "SEAHIR: A Specialized Compartmental Model for COVID-19," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
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