title: Stochastic, spatially-explicit epidemic models.
abstract: Animal disease epidemic models are useful for better understanding both the spread and control of disease in a population. While it is advisable that models be only as complex as needed, it is often necessary to modify simplifying assumptions and thus increase model complexity to better reflect reality. Here, the author will examine the need for increasing model complexity by including randomness in a model and modifying the assumption of homogeneous mixing, by introducing a spatial component into the model. The costs and benefits of these changes will be examined.
authors.affiliation: Center for Animal Disease Modeling and Surveillance, Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, One Shields Avenue, Davis, CA 95616, USA.
authors.forename: T E
authors.initials: TE
authors.lastname: Carpenter
mesh_terms: D000820:Animal Diseases / Q000453:epidemiology*; D000818:Animals; D016010:Binomial Distribution; D002140:California / Q000453:epidemiology; D003198:Computer Simulation; D003612:Dairying / Q000706:statistics & numerical data; D003710:Demography; D004196:Disease Outbreaks / Q000706:statistics & numerical data / Q000662:veterinary; D005536:Foot-and-Mouth Disease / Q000453:epidemiology; D008954:Models, Biological; D013269:Stochastic Processes