IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0298932.html
   My bibliography  Save this article

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

Author

Listed:
  • Yingze Hou
  • Hoda Bidkhori

Abstract

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Suggested Citation

  • Yingze Hou & Hoda Bidkhori, 2024. "Multi-feature SEIR model for epidemic analysis and vaccine prioritization," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-26, March.
    • Handle: RePEc:plo:pone00:0298932
    DOI: 10.1371/journal.pone.0298932
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298932
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298932&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0298932?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Glenn Ellison, 2020. "Implications of Heterogeneous SIR Models for Analyses of COVID-19," NBER Working Papers 27373, National Bureau of Economic Research, Inc.
    3. Fernando Alvarez & David Argente & Francesco Lippi, 2021. "A Simple Planning Problem for COVID-19 Lock-down, Testing, and Tracing," American Economic Review: Insights, American Economic Association, vol. 3(3), pages 367-382, September.
    4. Rabih Ghostine & Mohamad Gharamti & Sally Hassrouny & Ibrahim Hoteit, 2021. "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter," Mathematics, MDPI, vol. 9(6), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu Lu & Shaochong Lin & Zuo-Jun Max Shen & Junlong Zhang, 2025. "Location planning, resource reallocation and patient assignment during a pandemic considering the needs of ordinary patients," Health Care Management Science, Springer, vol. 28(2), pages 234-258, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    2. Talal Daghriri & Michael Proctor & Sarah Matthews, 2022. "Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    3. Pol Antràs & Stephen J. Redding & Esteban Rossi-Hansberg, 2023. "Globalization and Pandemics," American Economic Review, American Economic Association, vol. 113(4), pages 939-981, April.
    4. Lazebnik, Teddy, 2023. "Computational applications of extended SIR models: A review focused on airborne pandemics," Ecological Modelling, Elsevier, vol. 483(C).
    5. Arbex, Marcelo & Barros, Luiz A. & Corrêa, Márcio V., 2024. "Pandemic, inequality and public health: A quantitative analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    6. Goodkin-Gold, Matthew & Kremer, Michael & Snyder, Christopher M. & Williams, Heidi, 2022. "Optimal vaccine subsidies for endemic diseases," International Journal of Industrial Organization, Elsevier, vol. 84(C).
    7. Giagheddu, Marta & Papetti, Andrea, 2023. "The macroeconomics of age-varying epidemics," European Economic Review, Elsevier, vol. 151(C).
    8. Xiao Chen & Hanwei Huang & Jiandong Ju & Ruoyan Sun & Jialiang Zhang, 2022. "Endogenous cross-region human mobility and pandemics," CEP Discussion Papers dp1860, Centre for Economic Performance, LSE.
    9. Mohamed M. Mousa & Fahad Alsharari, 2021. "A Comparative Numerical Study and Stability Analysis for a Fractional-Order SIR Model of Childhood Diseases," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    10. David Desmarchelier & Thomas Lanzi, 2023. "Opinion Dynamics and Political Persuasion," Post-Print hal-04711036, HAL.
    11. Pirayesh, Amir & Asadaraghi, Alireza & Mohammadi, Mehrdad & Siadat, Ali & Battaïa, Olga, 2025. "A dynamic optimization model for vaccine allocation with age considerations: A study inspired by the COVID-19 pandemic," International Journal of Production Economics, Elsevier, vol. 280(C).
    12. Dong, Zhanyu & Cai, Jiayi & Li, Xuchao & Luan, Mengna, 2025. "Firm-level impacts and recovery dynamics following a public health crisis: Lessons from China’s SARS experience," Journal of Asian Economics, Elsevier, vol. 98(C).
    13. Brotherhood, Luiz & Cavalcanti, Tiago & Da Mata, Daniel & Santos, Cezar, 2022. "Slums and pandemics," Journal of Development Economics, Elsevier, vol. 157(C).
    14. Mart n Gonzales-Eiras, Dirk Niepelt, 2023. "Optimal Epidemic Control," Diskussionsschriften dp2311, Universitaet Bern, Departement Volkswirtschaft.
    15. Anton I. Votinov & Julia A. Polshchikova & Karen A. Nersisyan, 2025. "Macroeconomic Modeling in Post-pandemic Times," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 62-73, February.
    16. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    17. Mahmoud H. DarAssi & Mohammad A. Safi & Morad Ahmad, 2021. "Global Dynamics of a Discrete-Time MERS-Cov Model," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    18. Giorgio Fabbri & Salvatore Federico & Davide Fiaschi & Fausto Gozzi, 2024. "Mobility decisions, economic dynamics and epidemic," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(1), pages 495-531, February.
    19. Yuhei Miyauchi & Kentaro Nakajima & Stephen J. Redding, 2021. "The Economics of Spatial Mobility: Theory and Evidence Using Smartphone Data," NBER Working Papers 28497, National Bureau of Economic Research, Inc.
    20. Lin William Cong & Ke Tang & Bing Wang & Jingyuan Wang, 2021. "An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States," Papers 2109.10009, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0298932. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.