LEADER 03939nam 2200505z- 450 001 9910220111103321 005 20170919220144.0 010 $a0-8330-9630-3 035 $a(CKB)3710000000761960 035 $a(EXLCZ)993710000000761960 100 $a20160801c2016uuuu -u- - 101 0 $aeng 200 10$aImproving decision support for infectious disease prevention and control $ealigning models and other tools with policymakers' needs /$fDavid Manheim, Margaret Chamberlin, Osonde A. Osoba, Raffaele Vardavas, Melinda Moore 210 $cRAND Corporation 311 $a0-8330-9550-1 327 $aDecision support: a collaborative endeavor -- Decision support using models -- Nonmodeling decision-support approaches -- Alignment between policy questions and decision-support approaches -- Recommendations and discussion. 330 $a"This report describes decision-support tools, including models and nonmodeling approaches, that are relevant to infectious disease prevention, detection, and response and aligns these tools with real-world policy questions that the tools can help address. The intended audience includes technical experts - for example, modelers and subject-matter experts - and the policymakers that those experts can support. On one hand, this overview should help modelers and other technical experts understand the questions that policymakers will raise and the decisions they must make. On the other hand, many policymakers can benefit from a basic understanding of the capabilities and limitations of the different tools that may inform their decisions. This report describes the characteristics, requirements, uses, applicability, and limitations of three classes of theory-based models (population, microsimulation, agent-based simulation) and two classes of statistical models (regression-based and machine-learning), as well as several complementary nonmodeling decision-support approaches. The report then aligns all of these tools and approaches with a set of real-world policy questions. Finally, based on a review of published literature, an assessment of the different models and nonmodeling approaches, and recent experiences (such as the 2009 influenza pandemic), the authors recommend nine best practices for using modeling and decision-support tools to inform policymaking"--$cBack cover. 517 $aImproving Decision Support for Infectious Disease Prevention and Control 606 $aCommunicable diseases$xPrevention 606 $aCommunicable diseases$xControl 606 $aCommunicable diseases$xPrevention$xInternational cooperation 606 $aCommunicable diseases$xGovernment policy$zUnited States 606 $aCommunicable diseases$xControl$2fast$3(OCoLC)fst00869887 606 $aCommunicable diseases$xGovernment policy$2fast$3(OCoLC)fst00869894 606 $aCommunicable diseases$xPrevention$2fast$3(OCoLC)fst00869910 606 $aCommunicable diseases$xPrevention$xInternational cooperation$2fast$3(OCoLC)fst00869912 607 $aUnited States$2fast 615 0$aCommunicable diseases$xPrevention. 615 0$aCommunicable diseases$xControl. 615 0$aCommunicable diseases$xPrevention$xInternational cooperation. 615 0$aCommunicable diseases$xGovernment policy 615 7$aCommunicable diseases$xControl. 615 7$aCommunicable diseases$xGovernment policy. 615 7$aCommunicable diseases$xPrevention. 615 7$aCommunicable diseases$xPrevention$xInternational cooperation. 700 $aManheim$b David$01246530 702 $aChamberlin$b Margaret 702 $aOsoba$b Osonde 702 $aVardavas$b Raffaele 702 $aMoore$b Melinda 712 02$aNational Defense Research Institute (U.S.).$bForces and Resources Policy Center. 906 $aBOOK 912 $a9910220111103321 996 $aImproving decision support for infectious disease prevention and control$92890229 997 $aUNINA