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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » ABADRU » Research » Research Project #427647

Research Project: Modeling Japanese Encephalitis in US using Interconnected Networks

Location: Arthropod-borne Animal Diseases Research

Project Number: 3020-32000-010-04-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2014
End Date: Aug 31, 2019

Objective:
Japanese encephalitis (JE) is an infectious disease that is caused by a virus transmitted by mosquitoes. Domestic and feral pigs, some species of birds, and humans are all involved in the transmission cycle of this very serious zoonosis. JE is endemic in some areas of Asia, where the major vector identified is Culex tritaeniorhynchus. Even though this specific mosquito is not present in US, all vectors competent for West Nile virus are potentially competent for JEV too, posing a serious threat for the US. Modeling JE presents major challenges, as all vector-borne zoonoses. Due to the complexities of multiple populations involved, the direct use of a network approach, similar to the one used by our group to model RVF [1], will lead to a large set of equations with many parameters to be estimated. To overcome this problem, the PI proposes to explore novel modeling approaches based on interconnected networks [2]. Interconnected networks are an abstract representation where two or more simple networks, possibly with different and separate dynamics, are coupled to each other. This approach allows the study of one portion of the system, taking into account the influence of other interconnected components, reducing the complexity of the model and the number of parameters considered at each step. Studying the potential patterns of spread and detecting areas at risk based on vectors and hosts abundance is critical, to be prepared developing mitigation strategies, in the event JE virus is introduced in the United States. The objective of this proposal is to define and parameterize scalable models for JE, based on multiple networks, describing domestic and feral swine, mosquito, human, and bird populations in selected areas of the United States. [1] L. Xue, H. M. Scott, L. W. Cohnstaedt, C. Scoglio, "A Network-Based Meta-Population Approach to Model Rift Valley Fever Epidemics" Journal of Theoretical Biology, Elsevier, Volume 306, Pages 129–144, 2012 [2] F. Darabi Sahneh, F. Chowdhury, C. Scoglio, “Effect of Coupling on the Epidemic Threshold in Interconnected Complex Networks: A Spectral Analysis”, In Proceedings of the 2013 American Control Conference, June 17 - 19, Washington, DC, USA 2013

Approach:
The PI will generate novel modeling approaches based on interconnected networks. Interconnected networks are an abstract representation where two or more simple networks, possibly with different and separate dynamics, are coupled to each other. This approach allows the study of one portion of the system, taking into account the influence of other interconnected components, reducing the complexity of the model and the number of parameters considered at each step. Model Approach We plan to model the spreading of JE as a Markov process on interconnected networks. The interconnected multiple networks represent domestic and feral swine, mosquito, human, and bird populations in selected areas of the United States. Nodes within a network will represent geo-located homogeneous sub-populations, and links represent the possibility of transmission of infection. Interconnections represent the level of contact among sub-populations of different species. Starting from an initial infection, the model will produce stochastic infection spreading scenarios evolving in space and time. Outcomes We plan to develop 1) the mechanistic JE model described above, 2) a software tool implementing the model with the flexibility to input multiple mitigation strategies, 3) a Bayesian estimator to identify model parameters from data and calibrate the model, 4) a visualization tool to display the output in time and space through maps of those selected areas of the United States.