COVID-19 has brought simulation modeling to the foreground of public discourse, as policy makers and stakeholders project the effects of different interventions to “flatten the curve.” Simulation models allow investigators to tinker with different conditions of the world to observe how they affect future outcomes; however, translating the models’ results to the public can be challenging and requires nuanced explanations of data sources, assumptions, and caveats.
In a recent editorial in Medical Decision Making, CHERISH Research Affiliate Bohdan Nosyk describes his team’s experience developing, translating, and disseminating results of an HIV treatment and prevention simulation modeling study in six cities with co-authors Research Affiliates Emanuel Krebs, Czarina Behrends, Daniel Feaster, and Brandon Marshall, and CHERISH Investigators Bruce R. Schackman, Janet Weiner and Zachary Meisel.
After publishing their findings, Nosyk and colleagues sought to present their city-specific HIV transmission, treatment, and prevention simulation models to local stakeholders in the six cities they investigated. They faced mixed reactions when initially presenting their results, and they found it challenging to communicate their findings to local health departments and other stakeholders in the context of these audience’s competing priorities, time constraints, and apprehensions about simulation models. This experience highlights the need to engage decision makers during model development in order to incorporate feasible intervention strategies, and it emphasizes the need to improve the way researchers communicate model calibration, results, and uncertainty to both decision makers and the public. Engaging with these audiences early would likely improve dissemination and sustainability.
The authors urge investigators to integrate dissemination science to drive best practices in communicating model results and underlying assumptions. In incorporating dissemination science, investigators will understand how to best spread and sustain evidence-based interventions, especially when time is of the essence.