Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making. (January 2016)

• Record Type: Journal Article
• Authors: Knight, Gwenan M.; Dharan, Nila J.; Fox, Gregory J.; Stennis, Natalie; Zwerling, Alice; Khurana, Renuka; Dowdy, David W.
• Is Part Of: International journal of infectious diseases. Volume 42(2016:Jan.)
• Page Start: 17
• Page End: 23
• Publication Date: 2016-01
• Subjects: Models -- theoretical -- Public health practice -- Tuberculosis; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2015.10.024
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: Mathematical models are under-utilized in public health. We discuss the current use of infectious disease modelling with a case study. We lay out the methods and limitations of modelling infectious diseases. We present a framework for improved interaction between public health and modellers. This could lead to more transparent and evidence-driven policy decisions. Summary: 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.