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

Abstract

The 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 re-evaluated 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.

Keywords: Models; Public health practice; Tuberculosis; theoretical.

Figure 1

Roles and challenges of infectious disease models for public health decision-making.

Figure 2

Proposed framework for interaction between infectious disease modellers and public health practice. Stakeholders in public health practice should identify the key relevant public health questions for modellers to address and the existing data that can be used to inform model structure and parameters. Once shared and communicated with modellers, further discussion should pertain to the relevant model structures best suited to address the question and the required inputs. Subsequently, through ongoing communication, evidence gaps can be identified and modelling outputs can be reviewed and understood in the context of model uncertainty and generalizability. Once the model is refined and finalized, the outputs can be used to support data-informed decision-making. In this way, long-term collaboration between public health practitioners and mathematical modellers can ensure that models have optimal impact on evidence-based public health decision-making.