IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2025y2025i1n4724377.html

Bayesian Analysis and Model Discrimination in Structured Epidemic Dynamics: Estimation and Identifiability of Mixing Mechanisms Using Temporal Observations

Author

Listed:
  • Muteb Faraj Alharthi

Abstract

This study presents Bayesian analysis and discrimination for the structured two levels of mixing stochastic susceptible–infective–removed (SIR) epidemic model of infectious disease spread in a closed population, given temporal epidemic data. We focus on estimating key parameters, the global and local transmission rates (βG and βL), with a view to understanding their influence on disease dynamics. Utilizing Bayesian inference, we derive the joint posterior distribution of these parameters for both complete and incomplete outbreak data scenarios. In addition, we establish a simulation‐based method for discriminating between the two levels of mixing SIR models from other stochastic models and evaluate its performance through simulation experiments. Our methodologies provide valuable insights into disease transmission dynamics within and between households and can be extended to more complex models.

Suggested Citation

  • Muteb Faraj Alharthi, 2025. "Bayesian Analysis and Model Discrimination in Structured Epidemic Dynamics: Estimation and Identifiability of Mixing Mechanisms Using Temporal Observations," Journal of Mathematics, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jjmath:v:2025:y:2025:i:1:n:4724377
    DOI: 10.1155/jom/4724377
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/jom/4724377
    Download Restriction: no
    File URL: https://libkey.io/10.1155/jom/4724377?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. Banerjee, Malay & Ghosh, Samiran & Manfredi, Piero & d’Onofrio, Alberto, 2023. "Spatio-temporal chaos and clustering induced by nonlocal information and vaccine hesitancy in the SIR epidemic model," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Nikolaos Demiris & Philip D. O'Neill, 2005. "Bayesian inference for epidemics with two levels of mixing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 265-280, June.
    3. Muteb Faraj Alharthi & Ali Sajid, 2022. "The Basic Reproduction Number for the Markovian SIR-Type Epidemic Models: Comparison and Consistency," Journal of Mathematics, Hindawi, vol. 2022, pages 1-9, September.
    4. Muteb Faraj Alharthi, 2022. "The Basic Reproduction Number for the Markovian SIR‐Type Epidemic Models: Comparison and Consistency," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    5. Georgios Aristotelous & Theodore Kypraios & Philip D. O'Neill, 2025. "A classical hypothesis test for assessing the homogeneity of disease transmission in stochastic epidemic models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 52(1), pages 295-313, March.
    6. Tom Britton & Theodore Kypraios & Philip D. O'Neill, 2011. "Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 578-599, September.
    7. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    8. Nikolaos Demiris & Philip D. O'Neill, 2005. "Bayesian inference for stochastic multitype epidemics in structured populations via random graphs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 731-745, November.
    9. El Attouga, Sanae & El Khalifi, Mohamed, 2024. "Note on a Susceptible-Infectious-Recovered epidemic model with the heterogeneity in susceptibility," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    Full references (including those not matched with items on IDEAS)

    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. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    2. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    3. Chris Groendyke & David Welch & David R. Hunter, 2012. "A Network-based Analysis of the 1861 Hagelloch Measles Data," Biometrics, The International Biometric Society, vol. 68(3), pages 755-765, September.
    4. Tyagi, Swati & Martha, Subash C. & Abbas, Syed & Debbouche, Amar, 2021. "Mathematical modeling and analysis for controlling the spread of infectious diseases," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    5. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    6. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    7. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    8. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    9. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    10. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    11. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    12. Natalia Petrovskaya, 2025. "Is π a Chaos Generator?," Mathematics, MDPI, vol. 13(7), pages 1-25, March.
    13. Ofosuhene O Apenteng & Noor Azina Ismail, 2014. "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    14. Miguel Navascués & Costantino Budroni & Yelena Guryanova, 2021. "Disease control as an optimization problem," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-32, September.
    15. Frank Daumann & Florian Follert & Werner Gleißner & Endre Kamarás & Chantal Naumann, 2021. "Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management," IJERPH, MDPI, vol. 19(1), pages 1-23, December.
    16. M Gabriela M Gomes & Marc Lipsitch & Andrew R Wargo & Gael Kurath & Carlota Rebelo & Graham F Medley & Antonio Coutinho, 2014. "A Missing Dimension in Measures of Vaccination Impacts," PLOS Pathogens, Public Library of Science, vol. 10(3), pages 1-3, March.
    17. Yuan, Liang & Wu, Jiao & Xu, Kesheng & Zheng, Muhua, 2025. "The recurrence of groups inhibits the information spreading under higher-order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    18. Hongru Du & Matthew V Zahn & Sara L Loo & Tijs W Alleman & Shaun Truelove & Bryan Patenaude & Lauren M Gardner & Nicholas Papageorge & Alison L Hill, 2025. "Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback," PLOS Computational Biology, Public Library of Science, vol. 21(10), pages 1-24, October.
    19. Hamid Maroufy & Abdelati Lagzini, 2025. "Bayesian Inference for SIS Type Epidemic Model by Skellam’s Distribution with Real Application to COVID-19," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(3), pages 657-682, December.
    20. Wiriya Mahikul & Somkid Kripattanapong & Piya Hanvoravongchai & Aronrag Meeyai & Sopon Iamsirithaworn & Prasert Auewarakul & Wirichada Pan-ngum, 2020. "Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand," IJERPH, MDPI, vol. 17(7), pages 1-11, March.

    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:wly:jjmath:v:2025:y:2025:i:1:n:4724377. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.