IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i18p3874-d1237573.html
   My bibliography  Save this article

The Universal Theory for Multiscale Modelling of Infectious Disease Dynamics

Author

Listed:
  • Winston Garira

    (Modelling Health and Environmental Linkages Research Group (MHELRG), Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa)

  • Kizito Muzhinji

    (Modelling Health and Environmental Linkages Research Group (MHELRG), Department of Mathematical and Computational Sciences, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa)

Abstract

The replication-transmission relativity theory, currently used to inform the development of multiscale models of infectious disease dynamics, needs a revision and extension to accommodate new basic science and clinical information about infectious disease dynamics. In this article, we revise and extend the replication-transmission relativity theory into a new scientific theory of infectious disease dynamics called the universal theory for the multiscale modelling of infectious disease dynamics. This new theory states that, for every host–pathogen interaction that results in an infectious disease system, there is no privileged or absolute scale of a disease system form that would determine the dynamics of the infectious disease system, only interactions between the scales of a level of organisation of the pathogen-centred disease system form and the scales of the corresponding levels of organisation of the host-centred disease system form. We further explain the utility of this theory, which is reflected in its flexibility and ability to incorporate new information and explain previous information that could not be accounted for by the replication-transmission relativity theory of infectious disease dynamics.

Suggested Citation

  • Winston Garira & Kizito Muzhinji, 2023. "The Universal Theory for Multiscale Modelling of Infectious Disease Dynamics," Mathematics, MDPI, vol. 11(18), pages 1-40, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3874-:d:1237573
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/18/3874/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/18/3874/
    Download Restriction: no
    ---><---

    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. 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.
    7. 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.
    8. 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.
    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.

    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:gam:jmathe:v:11:y:2023:i:18:p:3874-:d:1237573. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.