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Network Science and Engineering Group

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3108 Engineering Hall
1701D Platt St.
Manhattan, KS 66506
785-532-5600
Fax: 785-532-1188

Hours: 8 am-12pm, 1pm-5pm M-F

Long-Distance Dispersal and Disease Outbreaks - NSF EEIDUSDA AFRI

Empirical data and modeling studies of diseases caused by pathogens with long-distance dispersal ability will be used to: 1) Determine effects of initial disease prevalence, spatial pattern of initial disease prevalence, and pathogen reproductive capacity on disease spread; 2) Compare the efficacy of reactive ring culling, reactive ring vaccination or chemotherapeutic applications, timing and extent of reactive ring treatments, and broad-scale population protection for disease control; and 3) Determine the influence of initial disease prevalence and pathogen reproductive capacity on the efficacy of these control tactics. Modeling studies of wheat stripe rust, foot-and-mouth disease, sudden oak death, and arboviruses of animals will be conducted. Extensive comparative modeling will be conducted through factorial combinations of models and input data among the different diseases. Generalized theory and models will be developed to predict "rules-of-thumb" for the control of diseases caused by pathogens with long-distance dispersal.

Duration September 1, 2015 - August 31 2021

Investigators

Faculty

Christopher Mundt (PI)

Lee Cohnstaedt (co-PI)

Ross Meentemeyer (co-PI)

Caterina Scoglio (Google Profile) (co-PI)

 

Students

Md Mahbubul Huq Riad (PhD)

Tanvir Ferdousi (PhD)

Sifat Afroj Moon (PhD)

 

Publications:

       1. Ferdousi T, Cohnstaedt L, McVey D, Scoglio C. Understanding the role of sexual transmission in the spread of ZIKA virus using an individual-based interconnected population model. Scientific Reports. 2019 May 10; 10.1038/s41598-019-43651-3.

       2. 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.

       3. Moon SA, Ferdousi T, Self A, Scoglio CM. Estimation of swine movement network at farm level in the US from the Census of Agriculture data. Scientific reports. 2019 Apr 17;9(1):6237.

      4. Ferdousi T, Moon SA, Self A, Scoglio C. Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus. PloS one. 2019;14(12).

      5. Riad MH, Sekamatte M, Ocom F, Makumbi I, Scoglio CM. Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network. Scientific reports. 2019 Nov 5;9(1):1-7.

      6. Riad MH. Network-based Modeling for Risk Assessment of Infectious Disease Transmission (Doctoral dissertation, Kansas State University) 2020 August.

      7.  Moon SA, Sahneh FD, Scoglio C. Group-based general epidemic modeling for spreading processes on networks: GroupGEM. IEEE Transactions on Network Science and Engineering. 2020 Nov 23.

      8. Riad MH, Cohnstaedt LW, Scoglio CM. Risk Assessment of Dengue Transmission in Bangladesh Using a Spatiotemporal Network Model and Climate Data. The American journal of tropical medicine and hygiene. 2021 Apr;104(4):1444.

      9. Moon SA. Modeling and analysis of epidemic processes over large networks from limited data (Doctoral dissertation) 2021 April.

    10. Ferdousi T. Computational models and tools for analysis, prediction, and control of infectious diseases (Doctoral dissertation) 2021 April.

Presentations:

  1. "An individual-level network model for a hypothetical outbreak of Japanese Encephalitis in USA", April 2016, Oregon State University.
  2. "Forecasting Infectious Disease Outbreak using Filtering Framework", February 2018, AMCA (American mosquito control association) 85th  annual meeting, Kansas city.
  3. "Understanding the role of sexual transmission in the spread of ZIKA virus using an individual-based interconnected population model", February 2018, AMCA (American mosquito control association) 85th  annual meeting, Kansas city.
  4. "A spatio-temporal individual-based network framework for West Nile virus in the USA: spreading pattern of West Nile virus", February 2018, AMCA (American mosquito control association) 85th  annual meeting, Kansas city.
  5. "Forecasting Infectious Disease Outbreak using Filtering Framework", May 2018, Oregon State University.
  6. "Understanding the survival of Zika virus in a vector interconnected sexual contact network.", May 2018, Oregon State University.
  7. "Understanding of the spreading pattern of West Nile virus in the USA using approximate Bayesian computation", May 2018, Oregon State University.
  8. "Understanding the long distance dispersal pattern of West Nile Virus", March 2019, ECEDHA conference, Arizona. 
  9.  "Network-based Modeling for Risk Assessment of Infectious Disease Transmission", August 2020, Department of Electrical and Computer Engineering, Kansas State University.
  10. "Modeling and analysis of epidemic processes over large networks from limited data", April 2021, Department of Electrical and Computer Engineering, Kansas State University.
  11. "Computational models and tools for analysis, prediction, and control of infectious diseases", April 2021, Department of Electrical and Computer Engineering, Kansas State University.

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Supported by United State Department of Agriculture under AFRI competitive grant 2015-67013-23818. Any opinions, findings, and conclusions or recommendations expressed in this website are those of the authors and do not necessarily reflect the views of the USDA.