Web-based models to inform health policy: A scoping review

Jade D. Rae, Winnie Chen, Sophie Diarra, Nhung Nghiem, Rebecca H. Chisholm, An Tran-Duy, Freya Shearer, Angela Devine

Research output: Contribution to journalReview articlepeer-review

Abstract

Health policies must be continually updated as new evidence is generated to ensure the optimal delivery of health interventions and prevention measures. Models are often used to study health problems, but their complexity limits their use by policy-makers. One way to facilitate their use among non-modellers is to develop user-friendly interfaces and make them available online. We conducted a scoping review of journal articles to identify and describe the currently available, interactive, freely available web-based health models that aim to inform health policy relevant to any disease or health issue affecting human populations. This scoping review included 16 web-based models covering 13 diseases or health issues, of which the most common were coronavirus disease 2019 (COVID-19) and malaria. The most common model outputs were epidemiological indicators (14/16), such as case numbers, incidences, or results from diagnostic screening, followed by the cost of implementing the intervention or health measure of interest (10/16). Model validation was performed in 6 of the 16 studies by comparing the model results with the previously published evidence or comparing simulated outcomes with observed data. Sensitivity and scenario analyses were conducted for 62.5% of models (10/16); however, in most cases, the user can perform these analyses by changing the model parameters in the user interface. This review explores the potential of web-based models to support health policy decisions and resource allocation. Despite their limited number, the 16 interactive web-based health models provide valuable insights into various health issues, primarily infectious diseases. The usability of the currently available web-based health models is impacted by the accuracy of the model description, the ability of the user to alter parameter values and the model assumptions that limit their generalizability. Such models must be validated and incorporate appropriate sensitivity analyses to be reliable and helpful to policy-makers.
Original languageEnglish
Article number99
Number of pages12
JournalHealth Research Policy and Systems
Volume23
Issue number1
DOIs
Publication statusPublished - 2025

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