Showing factors relevant to study quality including histograms of a) numbers of participants with MS in individual studies and b) overall quality scores, and c) the reporting of individual factors contributing to the overall quality score.

<p>Showing factors relevant to study quality including histograms of a) numbers of participants with MS in individual studies and b) overall quality scores, and c) the reporting of individual factors contributing to the overall quality score.</p

Forest plot of the individual studies showing their effect sizes as correlation coefficients.

<p>Box sizes are inversely proportional to study variance. Manuscripts reporting “non-significant” results without a point estimate are represented by circles. Aggregate effect size: r = -0.30; 95% confidence interval: -0.34, -0.26.</p

Flowchart showing articles retrieved and considered at each stage of the review process.

<p>Flowchart showing articles retrieved and considered at each stage of the review process.</p

Challenges on the interaction of models and policy for pandemic control

The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future