Decision Support for Infectious Disease Control
2016
This report from RAND reviews decision-support tools, including models and nonmodeling approaches, that are relevant to infectious disease prevention, detection, and response and aligns these tools with real-world policy questions that the tools can help address. This overview is designed to help modelers and other technical experts understand the questions that policymakers will raise and the decisions they must make. The report also presents policymakers with the capabilities and limitations of the different tools that may inform their decisions.
This report describes the characteristics, requirements, uses, applicability, and limitations of three classes of theory-based models (population, microsimulation, agent-based simulation) and two classes of statistical models (regression-based and machine-learning), as well as several complementary nonmodeling decision-support approaches. The report then aligns all of these tools and approaches with a set of real-world policy questions.
Finally, based on a review of published literature, an assessment of the different models and nonmodeling approaches, and recent experiences (such as the 2009 influenza pandemic), the authors recommend nine best practices for using modeling and decision-support tools to inform policymaking.
This research was conducted within the Forces and Resources Policy Center of the National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community.
Decision Support for Infectious Disease Control
Source:
Manheim D, Chamberlin M, Osoba O et al. Improving Decision Support for Infectious Disease Prevention and Control: Aligning Models and Other Tools with Policymakers' Needs. RAND Corporation 2016. https://www.rand.org/pubs/research_reports/RR1576.html