The COVID-19 pandemic has brought the combined disciplines of public health, infectious disease and policy modelling squarely into the spotlight
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

Objective:

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.

Findings:

The key consideration for policy-makers facing crises is not to discard models that cannot deliver evidence with very high certainty, but to recognize (1) features of models that indicate they will be useful, and (2) that the evidence generated by them is both timely and robust enough to be acted upon.

We therefore recommend that policy-makers undertake a “rapid appraisal” of models made available to them. In doing this, we recommend they consider three elements of model utility:

  1. instrumental utility, taking into account model

    (a) inputs

    (b) mechanisms, and

    (c) outputs;

  2. conceptual utility; and

  3. political utility.

View full paper here
First published: Nov 2, 2022
Population Interventions Unit
The Population Interventions Unit is a research group at the University of Melbourne that investigates health and cost impacts of population interventions.