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RTI International created a geospatial grid-based sampling methodology that: achieved a probability-based household sample and unbiased estimates; was comparable across countries; used simple frame data available from the in-country census; and was efficient with minimal training to local field staff. The methodology used 1 km[superscript 2] LandScan world population data, which was further subdivided to smaller grid cells ranging from 50 to 150 m[superscript 2]. To guide fieldwork, georeferenced maps were generated and viewed without an Internet connection using a third-party app and the tablet's internal global positioning systems receiver. The field staff's location was displayed in real time as a blue dot enabling field staff to determine household inclusivity. This methodology was successfully implemented for household surveys Latin America, Africa, and Asia. The design is transferable, adaptable, scalable, and can be used anywhere in the world.

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) impact the health and productivity of forests. Here, we explored the potential impacts of these environmental stressors on ecosystem services provided by future forests in the contiguous U.S. We found that all stand-level services benefitted (+ 2.6 to 8.1%) from reductions in N+S deposition, largely attributable to positive responses to reduced S that offset the net negative effects of lower N levels. Sawtimber responded positively (+ 0.5 to 0.6%) to some climate change, but negatively (− 2.4 to − 3.8%) to the most extreme scenarios. Aboveground carbon (C) sequestration and forest diversity were negatively impacted by all modelled changes in climate. Notably, the most extreme climate scenario eliminated gains in all three services achieved through reduced deposition. As individual tree species responded differently to climate change and atmospheric deposition, associated services unique to each species increased or decreased under future scenarios. Our results suggest that climate change should be considered when evaluating the benefits of N and S air pollution policies on the services provided by U.S. forests. [ABSTRACT FROM AUTHOR]

The pervasive and potentially severe economic, social, and public health consequences of infectious disease in farmed animals require that plans be in place for a rapid response. Increasingly, agent-based models are being used to analyze the spread of animal-borne infectious disease outbreaks and derive policy alternatives to control future outbreaks. Although the locations, types, and sizes of animal farms are essential model inputs, no public domain nationwide geospatial database of actual farm locations and characteristics currently exists in the United States. This report describes a novel method to develop a synthetic dataset that replicates the spatial distribution of poultry farms, as well as the type and number of birds raised on them. It combines county-aggregated poultry farm counts, land use/land cover, transportation, business, and topographic data to generate locations in the conterminous United States where poultry farms are likely to be found. Simulation approaches used to evaluate the accuracy of this method when compared to that of a random placement alternative found this method to be superior. The results suggest the viability of adapting this method to simulate other livestock farms of interest to infectious disease researchers.

Communicable-disease transmission models are useful for the testing of prevention and intervention strategies. Agent-based models (ABMs) represent a new and important class of the many types of disease transmission models in use. Agent-based disease models benefit from their ability to assign disease transmission probabilities based on characteristics shared by individual agents. These shared characteristics allow ABMs to apply transmission probabilities when agents come together in geographic space. Modeling these types of social interactions requires data, and the results of the model largely depend on the quality of these input data. We initially generated a synthetic population for the United States, in support of the Models of Infectious Disease Agent Study. Subsequently, we created shared characteristics to use in ABMs. The specific goals for this task were to assign the appropriately aged populations to schools, workplaces, and public transit. Each goal presented its own challenges and problems; therefore, we used different techniques to create each type of shared characteristic. These shared characteristics have allowed disease models to more realistically predict the spread of disease, both spatially and temporally.

Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses.RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants.For this database, RTI developed the methods for generating synthesized households and personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes).In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models.Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.