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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » ABADRU » Research » Publications at this Location » Publication #343936

Research Project: Ecology and Control of Insect Vectors

Location: Arthropod-borne Animal Diseases Research

Title: An individual-level network model for a hypothetical outbreak of Japanese Encephalitis in the USA

item RIAD, MAHBUBUL - Kansas State University
item SCOGLIO, CATERINA - Kansas State University
item McVey, David
item Cohnstaedt, Lee

Submitted to: Stochastic Environmental Research and Risk Assessment (SERRA)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2017
Publication Date: 2/1/2017
Citation: Riad, M.H., Scoglio, C., McVey, D.S., Cohnstaedt, L.W. 2017. An individual-level network model for a hypothetical outbreak of Japanese Encephalitis in the USA. Stochastic Environmental Research and Risk Assessment (SERRA). 31:2.

Interpretive Summary: We developed a sequential Monte Carlo filter to estimate the states and the parameters in a stochastic model of Japanese Encephalitis (JE) spread in the Philippines. This method is particularly important for its adaptability to the availability of new incidence data. This method can also capture the variability in the incidence through time which is dependent upon various factors relating to the host species and the weather. Parameters estimated from the particle filter simulations show seasonal as well as yearly variations. The basic reproductive ratio fluctuations are in compliance with the endemicity of JE in the Philippines. The estimated basic reproductive ratio is comparable with other similar mosquito transmitted disease like Zika, dengue, and West Nile viruses.

Technical Abstract: Predicting how a disease will spread in a population is reliant on understanding the basic information about the disease, such as the duration of infection, how the pathogen is spread, who can get sick, etc. These parameters are known for common diseases such as the flu, but less common or newly emerged diseases are more difficult to predict. Therefore, we developed a method for estimating parameters from case data. The estimations use repetitive sampling or testing of the data to determine the most accurate parameters that describe the existing cases. In this case, the initial human case data was Japanese Encephalitis human case data from the Philippines. Our method captured the variability in the number of human cases through time and varied with external factors such as the weather. Ultimately we were able to predict the basic spread (reproductive ratio) of endemic transmission of Japanese Encephalitis in the Philippines.