Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemic
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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.; Stuart, R. M.
Handbook of Statistics. Elsevier, 2021. s. 291-326 (Handbook of Statistics, Bind 44).Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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TY - CHAP
T1 - Mathematical modeling as a tool for policy decision making
T2 - Applications to the COVID-19 pandemic
AU - Panovska-Griffiths, J.
AU - Kerr, C. C.
AU - Waites, W.
AU - Stuart, R. M.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Agent-based models
KW - COVID-19
KW - Epidemiological modeling
KW - Rule-based models
KW - SEIR models
UR - http://www.scopus.com/inward/record.url?scp=85100430946&partnerID=8YFLogxK
U2 - 10.1016/bs.host.2020.12.001
DO - 10.1016/bs.host.2020.12.001
M3 - Book chapter
AN - SCOPUS:85100430946
T3 - Handbook of Statistics
SP - 291
EP - 326
BT - Handbook of Statistics
PB - Elsevier
ER -
ID: 256722208