Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemic

J. Panovska-Griffiths*, C. C. Kerr, W. Waites, R. M. Stuart

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

27 Citations (Scopus)

Abstract

The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of mathematical modeling in advising scientific bodies and informing public policy making. Modeling allows a flexible theoretical framework to be developed in which different scenarios around spread of diseases and strategies to prevent it can be explored. This work brings together perspectives on mathematical modeling of infectious diseases, highlights the different modeling frameworks that have been used for modeling COVID-19 and illustrates some of the models that our groups have developed and applied specifically for COVID-19. We discuss three models for COVID-19 spread: the modified Susceptible-Exposed-Infected-Recovered model that incorporates contact tracing (SEIR-TTI model) and describes the spread of COVID-19 among these population cohorts, the more detailed agent-based model called Covasim describing transmission between individuals, and the Rule-Based Model (RBM) which can be thought of as a combination of both. We showcase the key methodologies of these approaches, their differences as well as the ways in which they are interlinked. We illustrate their applicability to answer pertinent questions associated with the COVID-19 pandemic such as quantifying and forecasting the impacts of different test-trace-isolate (TTI) strategies.

Original languageEnglish
Title of host publicationData Science
Subtitle of host publicationTheory and Applications
EditorsArni S.R. Srinivasa Rao, C.R. Rao
PublisherElsevier B.V.
Pages291-326
Number of pages36
ISBN (Print)9780323852005
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Publication series

NameHandbook of Statistics
Volume44
ISSN (Print)0169-7161

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Agent-based models
  • COVID-19
  • Epidemiological modeling
  • Rule-based models
  • SEIR models

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