Abstract

BackgroundDespite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions.MethodsThe consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors.ResultsThe guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question – the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection – the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation – standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability – between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results – results should be presented in an appropriate way to support decision-making; and (6) interpretation – results should be interpreted to inform the policy question.ConclusionThese guidelines should help researchers plan, conduct and report model comparisons of infectious diseases and related interventions in a systematic and structured manner for the purpose of supporting health policy decisions. Adherence to these guidelines will contribute to greater consistency and objectivity in the approach and methods used in multi-model comparisons, and as such improve the quality of modelled evidence for policy.

Highlights

  • Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available

  • Mathematical models play a pivotal role in supporting policy decisions about the deployment of health interventions by synthesising evidence about infectious disease epidemiology to provide estimates of the long-term, population-level health impact of interventions [1]

  • The guidelines aim to help researchers improve the completeness and transparency of reporting multi-model comparisons; this should enhance the quality and transparency of multi-model comparison studies and provide better tools for decision-making. Process and stakeholders These guidelines were requested by the Immunization and Vaccines-related Implementation Research Advisory Committee (IVIR-AC) from the World Health Organization (WHO) [16]

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Summary

Introduction

Despite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Mathematical models play a pivotal role in supporting policy decisions about the deployment of health interventions by synthesising evidence about infectious disease epidemiology to provide estimates of the long-term, population-level health impact of interventions [1]. As such, they can inform key questions regarding disease eradication, elimination and control [2]. The modelling of infectious diseases has unique complexities related to infectious disease transmission, herd immunity and sources of heterogeneity in rates of infection and disease These distinct characteristics of infectious diseases mean that disease interventions often have population-level effects that are complex and non-linear. Optimising decisions with regard to interventions, such as the groups to target or the level of coverage to aim for, often requires more analytically complex models, such as transmission dynamic models, that require more behavioural and epidemiological information to parameterise [3]

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