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Epidemiology Modelling

In: Machine Learning Perspectives of Agent-Based Models

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  • Arit Kumar Bishwas

    (PricewaterhouseCoopers)

  • Anand Rao

    (Carnegie Mellon University, Heinz College of Information Systems and Public Policy)

Abstract

The classic way of modeling the progression of an infectious disease has been called the SIR (Susceptible-Infected-Recovered) model. Further refinements of this types of models capture additional states like the SEIRD model (Susceptible, Exposed, Infected, Recovered, and Dead) that captures the exposure and death states. The COVID-19 pandemic has resulted in a number of these models—built for different countries—that capture additional states. We examine current literature in this rich area of epidemiological models including states for contact, quarantined, not quarantined, pre-symptomatic and pre-asymptomatic, symptomatic and asymptomatic states, hospitalization, and immunized. The more states of infection a model captures, the more it facilitates fine-grained decision-making. We review these new models and how they have been used during the pandemic to make spatio-temporal predictions on the progression of COVID19

Suggested Citation

  • Arit Kumar Bishwas & Anand Rao, 2025. "Epidemiology Modelling," Springer Books, in: Pedro Campos & Anand Rao & Joaquim Margarido (ed.), Machine Learning Perspectives of Agent-Based Models, chapter 0, pages 51-76, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-73354-3_3
    DOI: 10.1007/978-3-031-73354-3_3
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