IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007734.html
   My bibliography  Save this article

The research and development process for multiscale models of infectious disease systems

Author

Listed:
  • Winston Garira

Abstract

Multiscale modelling of infectious disease systems falls within the domain of complexity science—the study of complex systems. However, what should be made clear is that current progress in multiscale modelling of infectious disease dynamics is still as yet insufficient to present it as a mature sub-discipline of complexity science. In this article we present a methodology for development of multiscale models of infectious disease systems. This methodology is a set of partially ordered research and development activities that result in multiscale models of infectious disease systems built from different scientific approaches. Therefore, the conclusive result of this article is a methodology to design multiscale models of infectious diseases. Although this research and development process for multiscale models cannot be claimed to be unique and final, it constitutes a good starting point, which may be found useful as a basis for further refinement in the discourse for multiscale modelling of infectious disease dynamics.Author summary: Complex systems such as infectious disease systems are inherently multilevel and multiscale systems. The study of such complex systems is called complexity science. In this article we present a methodology to design multiscale models of infectious disease systems from a complex systems perspective. Within this perspective we define complexity science as the study of the interconnected relationships of the levels and scales of organization of a complex system. We therefore, define the degree of complexity of a complex system as the number of levels and scales of organization of the complex system needed to describe it. In this work we first present a common multiscale vision of the multilevel and multiscale structure of infectious disease systems as complex systems in which the levels and scales of organization of an infectious disease system interact through different self-sustained multiscale cycles/loops (primary multiscale loops, or secondary multiscale loops, or tertiary multiscale loops) formed at different levels of organization of an infectious disease system due to ongoing reciprocal influence between the microscale and the macroscale. Guided by this multiscale vision, we propose a four-stage research and development process that result in multiscale models of infectious disease systems built from different scientific approaches.

Suggested Citation

  • Winston Garira, 2020. "The research and development process for multiscale models of infectious disease systems," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-39, April.
    • Handle: RePEc:plo:pcbi00:1007734
    DOI: 10.1371/journal.pcbi.1007734
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007734
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007734&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007734?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)

    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. Yifan Wu & Fan Zhang & Albert P. C. Chan & Dezhi Li, 2025. "Sentiment Evolution of Online Public Opinion of Emergency Situations in Railway Stations: A Case Study of Wuhan Railway Stations," Sustainability, MDPI, vol. 17(2), pages 1-29, January.
    7. Winston Garira & Kizito Muzhinji, 2023. "The Universal Theory for Multiscale Modelling of Infectious Disease Dynamics," Mathematics, MDPI, vol. 11(18), pages 1-40, September.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Shaheen A Abdulkareem & Ellen-Wien Augustijn & Tatiana Filatova & Katarzyna Musial & Yaseen T Mustafa, 2020. "Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.
    15. 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:pcbi00:1007734. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.