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Evaluation of Deaggregation Methods Used to Simulate Unknown Premises Locations for Disease Models: Cattle Disease Outbreaks in Argentina

Graduate #17
Discipline: Biological Sciences
Subcategory: Mathematics and Statistics
Session: 2
Room: Virginia A

Zavia Epps - Jackson State University
Co-Author(s): Frank Marrs, Colorado State University; Webb Research Lab, Colorado State University



This project focuses on the study of the 2001 foot-mouth-disease (FMD) outbreak of cattle in Argentina. The large scale of concentration is the utilization of a sample population, with the application of clustering algorithms for recreation of a data realization that can be used for aggregating and deaggregating farm sizes and farm locations. Within the Argentina dataset, there exact premises locations are unknown. The demographic information is aggregated at a Partido level, and infected premises (IP) are aggregated using a grid system. The grid system of the infected premises is smaller than the Partido level used to aggregate the demographic data. The aggregated IP grid reports the number of premises in the grid and the locations of the grid cells, but not the exact locations of premises in the grid. The development of the deaggregation method allows for the demographic and infection data to be deaggregated, through the creation of data sets with estimates of exact farm locations, that can be used in disease modeling.

Funder Acknowledgement(s): American Statistical Association; National Science Foundation

Faculty Advisor: Dr. Colleen Webb, colleen.webb@colostate.edu

Role: Coding; methods; simulations

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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