Modeling Ebola Spread and Using HPCC/KEL System



Epidemics have disturbed human lives for centuries causing massive numbers of deaths and illness among people and animals. Due to increase in urbanization, the possibility of worldwide epidemic is growing too. Infectious diseases like Ebola remain among the world’s leading causes of mortality and years of life lost. Addressing the significant disease burdens, which mostly impact the world’s poorest regions, is a huge challenge which requires new solutions and new technologies. This paper describes some of the models and mobile applications that can be used in determining the transmission, predicting the outbreak and preventing from an Ebola epidemic.


Healthcare Worker Mobile Application Case Fatality Rate Personal Protective Equipment Ordinary Differential Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been funded by the NSF Award No. CNS 1512932 RAPID: Modelling Ebola Spread and Developing Decision Support System Using Big Data Analytics, 2015–2016.


  1. 1.
    Browne C, Huo X, Magal P, Seydi M, Seydi O, Webb G. Model of 2014 Ebola Epidemic in West Africa with contact tracing.Google Scholar
  2. 2.
    Kouadio KI, Clement P, Bolongei J, Tamba A, Gasasira AN, Warsame A, Okeibunor JC, Ota MO, Tamba B, Gumede N, Shaba K, Poy A, Salla M, Mihigo R, Nshimirimana D. Epidemiological and surveillance response to Ebola virus disease outbreak in Lofa County, Liberia.Google Scholar
  3. 3.
    Lutwama JJ, Kamugisha J, Opio A, Nambooze J, Ndayimirije N, Okware S. Containing Hemorrhagic Fever Epidemic. The Ebola experience in Uganda.Google Scholar
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    Jin F, Dougherty E, Saraf P, Cao Y, Ramakrishnan N. Epidemiological modeling of news and rumors on Twitter.Google Scholar
  11. 11.
    Anastassopoulou SC, Russo L, Grigoras C, Mylonakis E. Modelling the 2014 Ebola virus epidemic—agent based simulations, temporal analysis and future predictions for Liberia and Sierra Leone.Google Scholar
  12. 12.
    Merler S, Ajeli M, Fumanelli L, Gomes MFC, Pastore y Piontti A, Rossi L, Chao DL, Longini IM, Halloran ME, Vespignani A. Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis.Google Scholar
  13. 13.
  14. 14.
    Meltzer MI, Atkins CY, Santibanez S, Knust B, Petersen BW, Ervin ED, Nichol ST, Damon IK, Washington ML. Estimating the future Number of cases in the Ebola Epidemic—Liberia and Sierra Leone, 2014–2015. Google Scholar
  15. 15.
    Zhang Z, Wang H, Wang C, Fang H. Modeling epidemics spreading on social contact networks.Google Scholar
  16. 16.
  17. 17.
    Haas CN. On the quarantine period for Ebola virus. PLoS Curr. 2014; Edition 1. doi: 10.1371/currents.outbreaks.2ab4b76ba7263ff0f084766e43abbd89.Google Scholar
  18. 18.
  19. 19.
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.
  25. 25.
  26. 26.
    Chowell G, Nishiura H. Transmission dynamics and control of Ebola virus disease (EVD): a review.Google Scholar
  27. 27.
  28. 28.
  29. 29.
    Centola D. The social origins of networks and diffusion. Am J Sociol. 2015;120(5):1295–1338. Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer and Electrical Engineering and Computer ScienceFlorida Atlantic UniversityBoca RatonUSA
  2. 2.LexisNexis Risk SolutionsAlpharettaUSA

Personalised recommendations