IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0221564.html
   My bibliography  Save this article

Why should we apply ABM for decision analysis for infectious diseases?—An example for dengue interventions

Author

Listed:
  • Florian Miksch
  • Beate Jahn
  • Kurt Junshean Espinosa
  • Jagpreet Chhatwal
  • Uwe Siebert
  • Nikolas Popper

Abstract

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.

Suggested Citation

  • Florian Miksch & Beate Jahn & Kurt Junshean Espinosa & Jagpreet Chhatwal & Uwe Siebert & Nikolas Popper, 2019. "Why should we apply ABM for decision analysis for infectious diseases?—An example for dengue interventions," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    • Handle: RePEc:plo:pone00:0221564
    DOI: 10.1371/journal.pone.0221564
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221564
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0221564&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0221564?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. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2017. "A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-2.
    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. Nguyen, Le Khanh Ngan & Howick, Susan & Megiddo, Itamar, 2024. "A framework for conceptualising hybrid system dynamics and agent-based simulation models," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1153-1166.

    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. Ali Asgary & Hudson Blue & Adriano O. Solis & Zachary McCarthy & Mahdi Najafabadi & Mohammad Ali Tofighi & Jianhong Wu, 2022. "Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    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. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2020. "A Model for the Spread of Infectious Diseases in a Region," IJERPH, MDPI, vol. 17(9), pages 1-19, April.
    4. Jagoda Kaszowska-Mojsa & Przemyslaw Wlodarczyk, 2020. "To freeze or not to freeze? Epidemic prevention and control in the DSGE model with agent-based epidemic component," Lodz Economics Working Papers 3/2020, University of Lodz, Faculty of Economics and Sociology.
    5. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    6. Winston Garira & Kizito Muzhinji, 2023. "The Universal Theory for Multiscale Modelling of Infectious Disease Dynamics," Mathematics, MDPI, vol. 11(18), pages 1-40, September.
    7. Elizabeth Hunter & Brian Mac Namee & John Kelleher, 2018. "An open-data-driven agent-based model to simulate infectious disease outbreaks," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-35, December.
    8. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
    9. Pilar Hernández & Carlos Pena & Alberto Ramos & Juan José Gómez-Cadenas, 2021. "A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-22, February.
    10. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.
    11. Andrea Guazzini & Mirko Duradoni & Alessandro Lazzeri & Giorgio Gronchi, 2018. "Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving," Future Internet, MDPI, vol. 10(6), pages 1-17, June.
    12. Constanza Fosco & Felipe Zurita, 2021. "Assessing the short-run effects of lockdown policies on economic activity, with an application to the Santiago Metropolitan Region, Chile," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

    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:pone00:0221564. 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.