A framework for considering the utility of models when facing tough decisions in public health : a guideline for policy-makers

Jason Thompson, Roderick McClure, Nick Scott, Margaret Hellard, Romesh Abeysuriya, Rajith Vidanaarachchi, John Thwaites, Jeffrey V. Lazarus, John Lavis, Susan Michie, Chris Bullen, Mikhail Prokopenko, Sheryl L. Chang, Oliver M. Cliff, Cameron Zachreson, Antony Blakely, Tim Wilson, Driss Ait Ouakrim, Vijay Sundararajan

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight. Never before have decisions regarding public health measures and their impacts been such a topic of international deliberation, from the level of individuals and communities through to global leaders. Nor have models—developed at rapid pace and often in the absence of complete information—ever been so central to the decision-making process. However, after nearly 3 years of experience with modelling, policy-makers need to be more confident about which models will be most helpful to support them when taking public health decisions, and modellers need to better understand the factors that will lead to successful model adoption and utilization. We present a three-stage framework for achieving these ends.
Original languageEnglish
Article number107
Number of pages7
JournalHealth Research Policy and Systems
Volume20
Issue number1
DOIs
Publication statusPublished - 2022

Open Access - Access Right Statement

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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