Network Science and Engineering Group

Modeling Japanese Encephalitis in US using Interconnected Networks

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 will lead to a large set of equations with many parameters to be estimated. To overcome this problem, we are exploring novel modeling approaches based on interconnected networks. 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.

Duration September 5 2014 - August 31 2019

Investigators

Faculty and Scholar

Caterina Scoglio (Google Profile)
 Lee Cohnstaedt

PhD Student

Md Mahbubul Huq Riad

Publications:

  1. Riad, M.H., Sekamatte, M., Ocom, F. et al. Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network. Sci Rep 9, 16060 (2019) doi:10.1038/s41598-019-52501-1

  2. Riad MH, Scoglio CM, Cohnstaedt LW, McVey DS. Short-term forecast and dual state-parameter estimation for Japanese Encephalitis transmission using ensemble Kalman filter. In2019 American Control Conference (ACC) 2019 Jul 10 (pp. 3444-3449). IEEE.

  3. Sekamatte M, Riad MH, Tekleghiorghis T, Linthicum KJ, Britch SC, Richt JA, Gonzalez JP, Scoglio CM. Individual-based network model for Rift Valley fever in Kabale District, Uganda. PloS one. 2019 Mar 5;14(3):e0202721.

  4. Moon SA, Cohnstaedt LW, McVey DS, Scoglio CM. A spatio-temporal individual-based network framework for West Nile virus in the USA: spreading pattern of West Nile virus. PLoS computational biology. 2019 Mar 13;15(3):e1006875.

  5. Ferdousi, T., Cohnstaedt, L.W., McVey, D.S. et al. Understanding the survival of Zika virus in a vector interconnected sexual contact network. Sci Rep 9, 7253 (2019) doi:10.1038/s41598-019-43651-3
  6. Riad MH, C. Scoglio, D. Scott McVey, L. W. Cohnstaedt, "Estimation of parameters and basic reproductive ratio for Japanese Encephalitis transmission in the Philippines using a sequential Monte Carlo filter", First IEEE Conference on Control Technology and Applications, Kohala Coast, Hawaii, 2017.
  7. Riad MH, Scoglio CM, McVey DS, Cohnstaedt LW. An individual-level network model for a hypothetical outbreak of Japanese encephalitis in the USA. Stochastic Environmental Research and Risk Assessment. 2017 Feb 1;31(2):353-67.

Presentations:

  1. "An individual-level network model for a hypothetical outbreak of Japanese Encephalitis in USA", April 2016, Oregon State University.
  2. "Estimation of parameters and basic reproductive ratio for Japanese Encephalitis transmission in the Philippines using a sequential Monte Carlo filter", 2017, Kohala Coast, Hawaii
  3. Forecasting Japanese Encephalitis Transmission in the Philippines using a Novel Filtering Framework​, AMCA 2018, Kansas City, KS
  4. Short-term forecast and dual state-parameter estimation for Japanese Encephalitis transmission using ensemble Kalman filter, 2019, Philadelphia, PA

Outreach

Supported by the United States Department of Agriculture. Any opinions, findings, and conclusions or recommendations expressed in this website are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.