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Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

Authors :
Becker AD
Grantz KH
Hegde ST
Bérubé S
Cummings DAT
Wesolowski A
Source :
The Lancet. Digital health [Lancet Digit Health] 2021 Jan; Vol. 3 (1), pp. e41-e50. Date of Electronic Publication: 2020 Dec 07.
Publication Year :
2021

Abstract

The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.<br /> (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Details

Language :
English
ISSN :
2589-7500
Volume :
3
Issue :
1
Database :
MEDLINE
Journal :
The Lancet. Digital health
Publication Type :
Academic Journal
Accession number :
33735068
Full Text :
https://doi.org/10.1016/S2589-7500(20)30268-5