Policy makers responsible for managing measles and rubella immunization programs currently use a wide range of different vaccines formulations and immunization schedules. With endemic measles and rubella transmission interrupted in the region of the Americas, all five other regions of the World Health Organization (WHO) targeting the elimination of measles transmission by 2020, and increasing adoption of rubella vaccine globally, integrated dynamic disease, risk, decision, and economic models can help national, regional, and global health leaders manage measles and rubella population immunity. Despite hundreds of publications describing models for measles or rubella and decades of use of vaccines that contain both antigens (e.g., measles, mumps, and rubella vaccine or MMR), no transmission models for measles and rubella exist to support global policy analyses. We describe the development of a dynamic disease model for measles and rubella transmission, which we apply to 180 WHO member states and three other areas (Puerto Rico, Hong Kong, and Macao) representing >99.5% of the global population in 2013. The model accounts for seasonality, age-heterogeneous mixing, and the potential existence of preferentially mixing undervaccinated subpopulations, which create heterogeneity in immunization coverage that impacts transmission. Using our transmission model with the best available information about routine, supplemental, and outbreak response immunization, we characterize the complex transmission dynamics for measles and rubella historically to compare the results with available incidence and serological data. We show the results from several countries that represent diverse epidemiological situations to demonstrate the performance of the model. The model suggests relatively high measles and rubella control costs of approximately $3 billion annually for vaccination based on 2013 estimates, but still leads to approximately 17 million disability-adjusted life years lost with associated costs for treatment, home care, and productivity loss costs of approximately $4, $3, and $47 billion annually, respectively. Combined with vaccination and other financial cost estimates, our estimates imply that the eradication of measles and rubella could save at least $10 billion per year, even without considering the benefits of preventing lost productivity and potential savings from reductions in vaccination. The model should provide a useful tool for exploring the health and economic outcomes of prospective opportunities to manage measles and rubella. Improving the quality of data available to support decision making and modeling should represent a priority as countries work toward measles and rubella goals.

Even seemingly simple systems can produce complex dynamics, which leads management professionals to develop tools for training, monitoring, and improving performance. Management simulators provide useful insights about human behavior and interactions, while computational and informational decision support tools offer opportunities to reduce inconsistencies, errors, and non-optimal human choices, particularly for complex systems that involve multiple decision makers, uncertainty, variability, and time. We use the context of a popular management simulator that teaches students about the bullwhip effect (i.e., the beer distribution game) to explore an integrated decision analytic, control theory, and system dynamics approach to the game that recognizes the value of available (imperfect) information and considers the value of perfect information to provide the optimal strategy. Using a discrete event simulation, we characterize optimal decisions and overall team scores for the situation of actual available information and perfect information. We describe our implementation of the strategy in the field to win the 2007 beer game world championship played at the 25th conference of the International System Dynamics Society. This paper seeks to demonstrate that better understanding of the system and use of available information leads to significantly lower expected costs than identified in prior studies. Understanding complex systems and using information optimally may increase system stability and significantly improve performance, in some cases even without better information than already available.INDEX TERMS Value of information, beer distribution game, decision analysis, system dynamics, control theory.