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A Review of Multi‐Compartment Infectious Disease Models

Author

Listed:
  • Lu Tang
  • Yiwang Zhou
  • Lili Wang
  • Soumik Purkayastha
  • Leyao Zhang
  • Jie He
  • Fei Wang
  • Peter X.‐K. Song

Abstract

Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.

Suggested Citation

  • Lu Tang & Yiwang Zhou & Lili Wang & Soumik Purkayastha & Leyao Zhang & Jie He & Fei Wang & Peter X.‐K. Song, 2020. "A Review of Multi‐Compartment Infectious Disease Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 462-513, August.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:2:p:462-513
    DOI: 10.1111/insr.12402
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    References listed on IDEAS

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    Cited by:

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    2. Camille Delbrouck & Jennifer Alonso-García, 2024. "COVID-19 and Excess Mortality: An Actuarial Study," Risks, MDPI, vol. 12(4), pages 1-27, March.
    3. Mukherjee, Nayana & Smith?, Stacey R. & Haque, Mainul, 2023. "Spatio-temporal patterns resulting from a predator-based disease with immune prey," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. George Selgin, 2021. "The fiscal and monetary response to COVID‐19: What the Great Depression has – and hasn't – taught us," Economic Affairs, Wiley Blackwell, vol. 41(1), pages 3-20, February.
    5. Thul, Lawrence & Powell, Warren, 2023. "Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 325-338.

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