Master of Science / Department of Electrical and Computer Engineering / Caterina Maria Scoglio / Japanese Encephalitis (JE) is a vector-borne disease transmitted by mosquitoes and maintained in birds and pigs. An interconnected network model is proposed to examine the possible epidemiology of JE in the USA. Proposed JE model is an individual-level network model that explicitly considers the feral pig population and implicitly considers mosquitoes and birds in specific areas of Florida, North Carolina, and South Carolina. The virus transmission among feral pigs within a small geographic area (<60 sq mi areas) are modeled using two network topologies— fully connected and Erdos-Renyi networks. Connections between locations situated in different states (interstate links) are created with limited probability and based on fall and spring bird migration patterns. Simulation results obtained from the network models support the use of the Erdos-Renyi network because maximum incidence occurs during the fall migration period which is similar to the peak incidence of the closely related West Nile virus (WNV), another virus in the Japanese Encephalitis group (Flaviviridae) that is transmitted by both birds and mosquitoes. Simulation analysis suggested two important mitigation strategies: for low mosquito vectorial capacity, insecticidal spraying of infected areas reduces transmission and limits the outbreak to a single geographic area. Alternatively, in high mosquito vectorial capacity areas, birds rather than mosquitoes need to be removed/controlled.

The Bayesian Markov Chain Monte Carlo(MCMC) approach to inference is commonly used to estimate the parameters in spatial infectious disease models. However, such MCMC analyses can pose a hefty computational burden. Here we present new method to reduce the computing time cost in such MCMC analyses and study its usefulness. This method is based a round the discretization of the spatial parameters in the infectious disease model. A normal approximation of the posterior density of the output from the original model will be compared to that of the modified model, using the Kullback-Leibler(KL) divergence measure.