Impact of spatial clustering on disease transmission and optimal control.

Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales.

Synthesized Population Databases: A Geospatial Database of US Poultry Farms.

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.