IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v513y2026ics0304380025004144.html

Multi-agent simulation for dengue spread forecast: A case study for two Brazilian cities

Author

Listed:
  • Araújo, Carlos Victor Dantas
  • Usberti, Fábio Luiz
  • de Oliveira, Emily Brito
  • de Assis, Laura Silva
  • Cavellucci, Celso

Abstract

This study introduces a multi-agent-based simulation (MABS) methodology for modeling the transmission spread of the dengue virus. The proposed methodology provides a flexible and adaptable approach to simulate the spread of dengue, accounting for the complex interactions between human populations, mosquitoes, and the environment. By leveraging agent-based modeling techniques, we can capture the stochastic nature of disease transmission and explore the impact of various factors, such as human behavior and vector control interventions. The model’s ability to generate realistic scenarios, even in the face of limited data, makes it a valuable tool for understanding the epidemiology of dengue and informing public health strategies, thus, this approach can also serve as a visualization and decision-support tool. The effectiveness of the proposed MABS framework is validated through its application to the cities of Alto Santo and Limoeiro, Brazil. However, it is easy to adapt to other cities using basic geographic information and historical data to determine optimal parameters.

Suggested Citation

  • Araújo, Carlos Victor Dantas & Usberti, Fábio Luiz & de Oliveira, Emily Brito & de Assis, Laura Silva & Cavellucci, Celso, 2026. "Multi-agent simulation for dengue spread forecast: A case study for two Brazilian cities," Ecological Modelling, Elsevier, vol. 513(C).
    • Handle: RePEc:eee:ecomod:v:513:y:2026:i:c:s0304380025004144
    DOI: 10.1016/j.ecolmodel.2025.111428
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380025004144
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2025.111428?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.

    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:ecomod:v:513:y:2026:i:c:s0304380025004144. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.journals.elsevier.com/ecological-modelling .

    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.