SPATIAL INFECTION MODELING OF FOOT-AND-MOUTH DISEASE

Abstract

 This paper models a spatial infection process of foot-and-mouth disease as a form of Markov chain process, and proposes the estimation methodology for the spatial infection probabilities. To apply this methodology, it takes into account of the fact that the observed data are affected by 1) the possibility of infection during incubation period and 2) artificial policies such as preventive culling. The observed data is the information about the expression of foot-and-mouth disease of animals, but not the infectious states of animals; as a result, the time-series infection states of animals cannot be grasped perfectly. This paper proposes the methodology using Markov Chain Monte Carlo (MCMC) method to estimate the spatial infection model including such characteristics. In addition, the availability of the proposed methodology is verified using the case of the outbreak of foot-and-mouth disease in Miyazaki prefecture in 2010.