IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v324y2025i1p246-258.html
   My bibliography  Save this article

Formulating human risk response in epidemic models: Exogenous vs endogenous approaches

Author

Listed:
  • LeJeune, Leah
  • Ghaffarzadegan, Navid
  • Childs, Lauren M.
  • Saucedo, Omar

Abstract

The recent pandemic emphasized the need to consider the role of human behavior in shaping epidemic dynamics. In particular, it is necessary to extend beyond the classical epidemiological structures to fully capture the interplay between the spread of disease and how people respond. Here, we focus on the challenge of incorporating change in human behavior in the form of “risk response” into compartmental epidemiological models, where humans adapt their actions in response to their perceived risk of becoming infected. The review examines 37 papers containing over 40 compartmental models, categorizing them into two fundamentally distinct classes: exogenous and endogenous approaches to modeling risk response. While in exogenous approaches, human behavior is often included using different fixed parameter values for certain time periods, endogenous approaches seek for a mechanism internal to the model to explain changes in human behavior as a function of the state of disease. We further discuss two different formulations within endogenous models as implicit versus explicit representation of information diffusion. This analysis provides insights for modelers in selecting an appropriate framework for epidemic modeling.

Suggested Citation

  • LeJeune, Leah & Ghaffarzadegan, Navid & Childs, Lauren M. & Saucedo, Omar, 2025. "Formulating human risk response in epidemic models: Exogenous vs endogenous approaches," European Journal of Operational Research, Elsevier, vol. 324(1), pages 246-258.
  • Handle: RePEc:eee:ejores:v:324:y:2025:i:1:p:246-258
    DOI: 10.1016/j.ejor.2025.01.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221725000049
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2025.01.004?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Hazhir Rahmandad & John Sterman, 2022. "Quantifying the COVID‐19 endgame: Is a new normal within reach?," System Dynamics Review, System Dynamics Society, vol. 38(4), pages 329-353, October.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Duggan, Jim & Andrade, Jair & Murphy, Thomas Brendan & Gleeson, James P. & Walsh, Cathal & Nolan, Philip, 2024. "An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland's pandemic response," European Journal of Operational Research, Elsevier, vol. 313(1), pages 343-358.
    4. Hazhir Rahmandad & Ran Xu & Navid Ghaffarzadegan, 2022. "Enhancing long-term forecasting: Learning from COVID-19 models," PLOS Computational Biology, Public Library of Science, vol. 18(5), pages 1-15, May.
    5. Ramzi Hammami & Sinan Salman & Moutaz Khouja & Imen Nouira & Suzan Alaswad, 2023. "Government strategies to secure the supply of medical products in pandemic times," Post-Print hal-03997319, HAL.
    6. Kraft, Holger & Weiss, Farina, 2023. "Pandemic portfolio choice," European Journal of Operational Research, Elsevier, vol. 305(1), pages 451-462.
    7. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.
    8. Lane, David C. & Oliva, Rogelio, 1998. "The greater whole: Towards a synthesis of system dynamics and soft systems methodology," European Journal of Operational Research, Elsevier, vol. 107(1), pages 214-235, May.
    9. Hammami, Ramzi & Salman, Sinan & Khouja, Moutaz & Nouira, Imen & Alaswad, Suzan, 2023. "Government strategies to secure the supply of medical products in pandemic times," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1364-1387.
    10. Christine S.M. Currie & John W. Fowler & Kathy Kotiadis & Thomas Monks & Bhakti Stephan Onggo & Duncan A. Robertson & Antuela A. Tako, 2020. "How simulation modelling can help reduce the impact of COVID-19," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 83-97, April.
    11. Hazhir Rahmandad & Tse Yang Lim & John Sterman, 2021. "Behavioral dynamics of COVID‐19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations," System Dynamics Review, System Dynamics Society, vol. 37(1), pages 5-31, January.
    12. Kassa, Semu M. & Njagarah, John B.H. & Terefe, Yibeltal A., 2020. "Analysis of the mitigation strategies for COVID-19: From mathematical modelling perspective," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    13. Fattahi, Mohammad & Keyvanshokooh, Esmaeil & Kannan, Devika & Govindan, Kannan, 2023. "Resource planning strategies for healthcare systems during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 192-206.
    14. Daniel Gordon & Ali N. Mashayekhi & Andrada Tomoaia‐Cotisel & Hyunjung Kim & Babak Bahaddin & Luis F. Luna‐Reyes & David F. Andersen, 2024. "Developing model‐based storytelling to share systemic insights to the public during the COVID‐19 pandemic," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    15. Neil Ferguson, 2007. "Capturing human behaviour," Nature, Nature, vol. 446(7137), pages 733-733, April.
    16. Eryarsoy, Enes & Shahmanzari, Masoud & Tanrisever, Fehmi, 2023. "Models for government intervention during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 69-83.
    17. d'Onofrio, Alberto & Manfredi, Piero, 2022. "Behavioral SIR models with incidence-based social-distancing," Chaos, Solitons & Fractals, Elsevier, vol. 159(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. Jiang, Jiehui & Sheng, Dian & Chen, Xiaojing & Tian, Qiong & Li, Feng & Yang, Peng, 2024. "Data-driven collaborative healthcare resource allocation in pandemics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    2. Neng Shen & Jing Zhang & Yang Chun Cao & Lin Zhang & Guoping Zhang, 2025. "Clear the fog: Can public–private collaborative supervision promote the construction of a high‐quality public health system?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(1), pages 52-66, January.
    3. Boun My, Kene & Nguyen-Van, Phu & Kim Cuong Pham, Thi & Stenger, Anne & Tiet, Tuyen & To-The, Nguyen, 2022. "Drivers of organic farming: Lab-in-the-field evidence of the role of social comparison and information nudge in networks in Vietnam," Ecological Economics, Elsevier, vol. 196(C).
    4. Duggan, Jim & Andrade, Jair & Murphy, Thomas Brendan & Gleeson, James P. & Walsh, Cathal & Nolan, Philip, 2024. "An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland's pandemic response," European Journal of Operational Research, Elsevier, vol. 313(1), pages 343-358.
    5. Navid Ghaffarzadegan & Aritra Majumdar & Ross Williams & Niyousha Hosseinichimeh, 2024. "Generative agent‐based modeling: an introduction and tutorial," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    6. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    7. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    8. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    9. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    10. G.J. Melman & A.K. Parlikad & E.A.B. Cameron, 2021. "Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation," Health Care Management Science, Springer, vol. 24(2), pages 356-374, June.
    11. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    12. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    13. Ma, Peng, 2021. "Optimal generic and brand advertising efforts in a decentralized supply chain considering customer surplus," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    14. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    15. Klingler, Anna-Lena & Luthander, Rasmus, 2018. "Market diffusion of residential PV and battery systems driven by self-consumption: A comparison of Sweden and Germany," Working Papers "Sustainability and Innovation" S18/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    16. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "A segment-based analysis of Internet service adoption among UK households," Technology in Society, Elsevier, vol. 29(3), pages 339-350.
    17. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    18. Liberali, Guilherme & Gruca, Thomas S. & Nique, Walter M., 2011. "The effects of sensitization and habituation in durable goods markets," European Journal of Operational Research, Elsevier, vol. 212(2), pages 398-410, July.
    19. Chul-Yong Lee & Jongsu Lee, 2009. "Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update," TEMEP Discussion Papers 200903, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2009.
    20. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:ejores:v:324:y:2025:i:1:p:246-258. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.