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Modelling epidemic spreading in structured organisations

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  • Kuikka, Vesa

Abstract

Modelling epidemic spreading in a population or in organisations is important in planning preventive measures and allocating resources for treating infected individuals in hospitals. We present a structural spreading model capable of describing detailed structures of organisations. We discuss methods and results with the help of an example organisation. The example organisation is a real-world organisation but our main focus is on presenting modelling approaches. Our spreading model is designed for describing indirect virus spreading mechanics via respiratory droplets and aerosols from an infected person rather than spreading via physical person to person contacts. To this end, we propose a new complex contagion model that describes the spreading process alike a social interaction process. Different preventive measures and their combinations can be compared by our model. We show that the optimised preventive measures in the complex contagion model can be different from the corresponding simple contagion model. We study the effects of limiting contacts between different organisation structures and shortening chains of infection together with general risk mitigation actions. Out-centrality, in-centrality and betweenness measures are used in analysing different aspects of epidemic spreading. Examples of calculating community immunity are presented, in which strategies based on out-centrality and betweenness measures are prioritised.

Suggested Citation

  • Kuikka, Vesa, 2022. "Modelling epidemic spreading in structured organisations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437122000164
    DOI: 10.1016/j.physa.2022.126875
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    References listed on IDEAS

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    1. Matthew Koehler & David M Slater & Garry Jacyna & James R Thompson, 2021. "Modeling COVID-19 for Lifting Non-Pharmaceutical Interventions," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(2), pages 1-9.
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    Cited by:

    1. Almiala, Into & Aalto, Henrik & Kuikka, Vesa, 2023. "Influence spreading model for partial breakthrough effects on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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