We began to develop an agent-based epidemiological model of animal disease propagation within the beef and dairy industries. Model development was done in context of a consortium of interested parties including the New Mexico State University (NMSU) Extension Service, the New Mexico Livestock Board (NMLB), ranchers representing the beef industry, and farmers representing the dairy industry. The model required a thorough understanding of the life cycles for commodity livestock, especially the transportation and mixing that occurs as part-and-parcel of how production and commerce is practiced. Once this detailed network of animal movement and interaction is articulated within the model, we can simulate the introduction of disease at any given location and track its propagation. The model will serve to understand how inter-operation transfer of livestock can impact the likelihood and magnitude of infectious disease outbreaks. With this understanding, the cost-effectiveness of current and proposed prevention and monitoring strategies, as well as mitigation strategies, can be assessed. Our first focus for model application may be the propagation of bovine tuberculosis (TB) in beef cattle. In subsequent work, we would like to expand the computational model to include dairy cattle, and to consider the propagation of other diseases such as foot-and-mouth disease (FMD) and Rift Valley fever.
Physicians are far from optimal decision makers: they overuse defensive medical practices such as medical tests (bias), and they disagree on their diagnoses and treatments (practice variation). Besides the regional factors (such as culture), at the individual level, the most common explanations for these phenomena are linked to physicians personality traits (e.g., risk aversion) or their financial incentives. We develop a theory that offers a new explanation. With the help of a simulation model, we show that practice variation and bias does not have to be caused by personality traits and financial incentives, but can endogenously emerge through daily practices and outcome learning even for physicians with similar trainings working in the same region. Specifically, a physicians exposure to outcome feedback and her accumulated experience and skill contribute to variation and bias. A preliminary validation is achieved by comparing simulation results with the data from c-section surgery in the states of New York and Florida.
Nearly 3 billion people around the world use solid biomass, such as fuelwood and crop waste, for cooking and heating. The implementation of biogas, liquid petroleum gas, solar and other alternative energy cookstoves presents an opportunity to alleviate the burden of fuelwood collection and the health implications associated with inefficient biomass combustion while mitigating the negative ecological and climate effects of deforestation. Many governments and international development agencies have initiated programs to distribute alternative energy cookstoves, but the new technologies rarely achieve sustained use with consumers. While funds have been widely distributed to research the technical design of cookstove technologies, very little systematic research has been done to understand and improve implementation and use of the technologies in the complex markets they target.
Modeling Dynamics Systems: Lessons for a First Course (third edition) provides a set of materials that enable educators at the secondary and college levels to teach a one-semester or one-year course in System Dynamics modeling. These lessons are also useful for trainers in a business environment. Developed for beginning modelers, the lessons contained in this book can be used for a core curriculum or for independent study. The lessons include some of the classic System Dynamics problems (population change, resource sustainability, drug pharmacokinetics, spread of an epidemic, urban growth, supply and demand, and more). Feedback analysis is integral to the lessons. Guidelines for an independent project and an outline for a technical paper explaining the creation process and structure of the final model, together with scoring guides for both the model and the paper, are included. Participants in the workshop will have a chance to build some simple models (participants should bring laptops) and gain a sense of the progression leading to a more sophisticated model. Student work will be demonstrated and can also be viewed at www.ccmodelingsystems.com. New materials in the third edition (oscillations, transfer of loop dominance, mapping systems in the news, ) of this book will be presented.
This paper describes work in progress on Houdini: a system dynamics model of the Dutch housing market focused on explaining institutional structures leading to high price increases and obstructing new supply and on
This paper presents an application of system dynamics to understand the behavior of the AH-64 Advanced Aircraft Course at the U.S. Army Aviation Center of Excellence. This course trains Army lieutenants and warrant officers as combat aviators. In the last several years, a large bubble of students awaiting the different phases of training has developed because of organizational and process problems within the course. This paper presents a system dynamics model of the course and recommends policy changes to eliminate the backlog of students awaiting training. The model incorporates both the organizational aspects of the course, including personnel and equipment; as well as the processes within the course. Base on output from the system dynamics model, the best course of action for the U.S. Army Aviation Center of Excellence is to add additional days to the course to account for weather and increase the number of hours available for training on a daily basis. This policy enables the center to eliminate the bubble of students and stabilize the process of training combat aviators.
From June through October 2008 the National Guard Bureau (NGB)J8 conducted a capability based assessment (CBA) to determine National Guard (NG) capability gaps for Defense Support to Civil Authorities (DSCA). A major DCSA mission that the NGB Capability Assessment and Development Process (CADP) focused on was Chemical, Biological, Radiological, Nuclear, and high-Explosive Consequence Management (CBRNE CM) response. As expected, the most difficult portion of the CBA was defining and quantifying the gap in NG specialized CBRNE CM capabilities (CERFPs). Initially, NGB developed a simple allocation model that captured the total number of CERFPs employed based on subject matter expertise. However, NGB-J8 developed a more objectively quantifiable model that would systemically document and consistently apply assumptions. Repeatability was essential to defining and quantifying NGB CBRNE CM capability gaps. NGB-J8 determined a System Dynamics Model would be the best approach for developing this model.