title: 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.
authors.affiliation: National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance, Imperial College London, 8(th) floor Commonwealth Building, Hammersmith Hospital Campus, Du Cane Road, London, W12 0HS, UK; TB Modelling Group, TB Centre, Centre for Mathematical Modelling, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. Electronic address: g.knight@imperial.ac.uk.
authors.forename: Gwenan M
authors.initials: GM
authors.lastname: Knight
authors.affiliation: New Jersey Medical School - Rutgers, the State University of New Jersey, Newark, New Jersey, USA.
authors.forename: Nila J
authors.initials: NJ
authors.lastname: Dharan
authors.affiliation: Respiratory Epidemiology Clinical Research Unit, McGill University, Montreal, Quebec, Canada.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Fox
authors.affiliation: New York City Department of Health and Mental Hygiene, Bureau of Tuberculosis Control, New York, New York, USA.
authors.forename: Natalie
authors.initials: N
authors.lastname: Stennis
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
authors.forename: Alice
authors.initials: A
authors.lastname: Zwerling
authors.affiliation: Maricopa County Department of Public Health, Clinical Services, Phoenix, Arizona, USA.
authors.forename: Renuka
authors.initials: R
authors.lastname: Khurana
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
mesh_terms: D003141:Communicable Diseases / Q000628:therapy*; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D011634:Public Health
keywords: Models; Public health practice; Tuberculosis; theoretical