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

doi:10.1016/bs.host.2020.12.001

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 proceeding › Chapter

39 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 language	English
Title of host publication	Data Science
Subtitle of host publication	Theory and Applications
Editors	Arni S.R. Srinivasa Rao, C.R. Rao
Place of Publication	Amsterdam
Publisher	Elsevier B.V.
Pages	291-326
Number of pages	36
ISBN (Print)	9780323852005
DOIs	https://doi.org/10.1016/bs.host.2020.12.001
Publication status	Published - 12 Feb 2021

Publication series

Name	Handbook of Statistics
Volume	44
ISSN (Print)	0169-7161

Funding

We thank all contributors to the development and application of the three models we discussed here. For the SEIR-TTI model we thank Simone Sturniolo, David Manheim and Tim Colbourn. For the Covasim model we thank a large team of collaborators: at the Institute for Disease Modeling, Daniel J. Klein, Dina Mistry, Brittany Hagedorn, Katherine Rosenfeld, Prashanth Selvaraj, Rafael Núñez, Gregory Hart, Carrie Bennette, Marita Zimmermann, Assaf Oron, Dennis Chao, Michael Famulare, and Lauren George; at GitHub, Michał Jastrzębski, Will Fitzgerald, Cory Gwin, Julian Nadeau, Hamel Husain, Rasmus Wriedt Larsen, Aditya Sharad, and Oege de Moor; at Microsoft, William Chen, Scott Ayers, and Rolf Harms; and at the Burnet Institute, Romesh Abeysuriya, Nick Scott, Anna Palmer, Dominic Delport, and Sherrie Kelly. For the RBM model, we thank Matteo Cavalieri and Vincent Danos.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

agent-based models
COVID-19
epidemiological modeling
rule-based models
SEIR models

Access to Document

10.1016/bs.host.2020.12.001

Cite this

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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.",

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Mathematical modeling as a tool for policy decision making: applications to the COVID-19 pandemic. / Panovska-Griffiths, J.; Kerr, C. C.; Waites, W. et al.
Data Science: Theory and Applications. ed. / Arni S.R. Srinivasa Rao; C.R. Rao. Amsterdam: Elsevier B.V., 2021. p. 291-326 (Handbook of Statistics; Vol. 44).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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

Abstract

Publication series

Funding

UN SDGs

Keywords

Access to Document

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