Epidemiology Modelling

Arit Kumar Bishwas () and Anand Rao ()
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Arit Kumar Bishwas: PricewaterhouseCoopers
Anand Rao: Carnegie Mellon University, Heinz College of Information Systems and Public Policy

Chapter Chapter 3 in Machine Learning Perspectives of Agent-Based Models, 2025, pp 51-76 from Springer

Abstract: 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

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-73354-3_3

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DOI: 10.1007/978-3-031-73354-3_3

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