Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making

Abstract

SUMMARYThe dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inform specific components of policy (such as efficacy), the same process is rarely applied to the full decision-making process. Mathematical models provide a mechanism through which empirical evidence can be methodically and transparently integrated to address such questions. However, such models are often considered difficult to interpret. In addition, models provide estimates that need to be iteratively reevaluated as new data or considerations arise. Using the case study of a novel diagnostic for tuberculosis, a framework for improved collaboration between public health decision-makers and mathematical modellers that could lead to more transparent and evidence-driven policy decisions for infectious diseases in the future is proposed. The framework proposes that policymakers should establish long-term collaborations with modellers to address key questions, and that modellers should strive to provide clear explanations of the uncertainty of model structure and outputs. Doing so will improve the applicability of models and clarify their limitations when used to inform real-world public health policy decisions.

Disease modeling for public health: added value, challenges, and institutional constraints

“…For researchers and policy advisors who compile and evaluate scientific evidence for health interventions, mathematical modeling has proven to be a useful tool. Developing a mathematical model helps to synthesize information from different sources into a consistent framework that allows an integrated analysis of complex problems [1,2]. Researchers in public health, who provide advice to policymakers, often use mathematical models to simulate the impact of various interventions or public health strategies, and to provide quantitative predictions of how interventions might affect population health in the future.…”

TIME Impact – a new user-friendly tuberculosis (TB) model to inform TB policy decisions

“…By engaging in-country policymakers in the modelling process, local ownership of the modelling methods and results can be increased, as the process requires an assessment of the data and epidemic, including existing gaps, and working through the data and assumptions for potential interventions. Together, these benefits strengthen the rational foundation of TB policy decisions in LMICs [ 12 ]. In addition, a user friendly interface would allow for building local capacity, where local TB experts can progress from being informed consumers of modelling results to independent users of the modelling tool.…”

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

“…They can also explore the feasibility of control strategies and support the implementation and evaluation of alternative intervention and control methods. According to Knight et al (2016) [ 73 ], results from models can be linked to public health policy in at least three ways: (i) “improve our understanding of infectious disease epidemic systems so that public health practitioners can better target key drivers of epidemic spread”; (ii) “evaluate and compare the potential epidemiological and economic impact of alternative public health interventions”; (iii) “reveal data gaps that, if filled, would enable public health officials to make more evidence-based decisions in the future”. Further discussions and applications can be found in [ 73 , 74 , 75 , 76 , 77 ].…”