Analyzing Global Epidemiology of Diseases Using Human-in-the-Loop Bio-Simulations

  • Dhananjai M. Rao
  • Alexander Chernyakhovsky
  • Victoria Rao


Humanity is facing an increasing number of highly virulent and communicable diseases such as influenza. Combating such global diseases requires in-depth knowledge of their epidemiology. The only practical method for discovering global epidemiological knowledge and identifying prophylactic strategies is simulation. However, several interrelated factors, including increasing model complexity, stochastic nature of diseases, and short analysis timeframes render exhaustive analysis an infeasible task. An effective approach to alleviate the aforementioned issues and enable efficient epidemiological analysis is to manually steer bio-simulations to scenarios of interest. Selective steering preserves causality, inter-dependencies, and stochastic characteristics in the model better than “seeding”, i.e., manually setting simulation state. Accordingly, we have developed a novel Eco-modeling and bio-simulation environment called SEARUMS. The bio-simulation infrastructure of SEARUMS permits a human-in-the-loop to steer the simulation to scenarios of interest so that epidemics can be effectively modeled and analyzed. This article discusses mathematical principles underlying SEARUMS along with its software architecture and design. In addition, the article also presents the bio-simulations and multi-faceted case studies conducted using SEARUMS to elucidate its ability to forecast timelines, epicenters, and socio-economic impacts of epidemics. Currently, the primary emphasis of SEARUMS is to ease global epidemiological analysis of avian influenza. However, the methodology is sufficiently generic and it can be adapted for other epidemiological analysis required to effectively combat various diseases.


Markov Process Avian Influenza Severe Acute Respiratory Syndrome Epidemiological Analysis Antiviral Prophylaxis 
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.


  1. Anderson RM, May RM (1992) Infectious diseases of humans: dynamics and control. Oxford University, Oxford University PressGoogle Scholar
  2. Bloch J (2001) Effective Java programming language guide, 1st edn. Pearson, Prentice Hall Google Scholar
  3. Booth G (1997) Gecko: a continuous 2d world for ecological modeling. Artif Life 3(3):147–163CrossRefGoogle Scholar
  4. CDC (2006) Centers for disease control & prevention: avian Influenza: current situation.
  5. Daley DJ, Gani J (2001) Epidemic modelling: an introduction. Cambridge University Press, OxfordzbMATHGoogle Scholar
  6. Epstein JM (2009) Modelling to contain pandemics. Nature 460:687–687. doi: 10.1038/nature460687a CrossRefGoogle Scholar
  7. Ferguson NM, Cummings DAT, Fraserl C, Cajka JC, Cooley PC, Burke DS (2006) Strategies for mitigating an influenza pandemic. Nature 442:448–452. doi: 10.1038/nature04795 CrossRefGoogle Scholar
  8. Flint SJ, Enquist LW, Racaniello VR, Skalka AM (2004) Principles of virology, 2nd edn. American Society for Microbiology (ASM), Washington, DCGoogle Scholar
  9. Garten RJ, Davis CT, Russell CA, Shu B, Lindstrom S, Balish A, Sessions WM, Xu X, Skepner E, Deyde V, Okomo-Adhiambo M, Gubareva L, Barnes J, Smith CB, Emery SL, Hillman MJ, Rivailler P, Smagala J, de Graaf M, Burke DF, Fouchier RAM, Pappas C, Alpuche-Aranda CM, Lopez-Gatell H, Olivera H, Lopez I, Myers CA, Faix D, Blair PJ, Yu C, Keene KM, Dotson JP David, Boxrud D, Sambol AR, Abid SH, St George K, Bannerman T, Moore AL, Stringer DJ, Blevins P, Demmler-Harrison GJ, Ginsberg M, Kriner P, Waterman S, Smole S, Guevara HF, Belongia EA, Clark PA, Beatrice ST, Donis R, Katz J, Finelli L, Bridges CB, Shaw M, Jernigan DB, Uyeki TM, Smith DJ, Klimov AI, Cox NJ (2009) Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans. Science 325(5937):197–201. doi:10.1126/science.1176225,
  10. Gilbert N, Bankes S (2002) Platforms and methods for agent-based modeling. In: Proceedings of the National Academy of Sciences of the USA, vol 99, pp 7197–7198. doi:10.1073/pnas.072079499
  11. GLiPHA (2007) Global livestock production and health atlas (GLiPHA): animal production and health division of food and agriculture organization of the UN.
  12. GROMS (2006) Global register of migratory species: summarising knowledge about migratory species for conservation.
  13. Gu W, Vetter J, Schwan K (1994) An annotated bibliography of interactive program steering. ACm SIGPLAN notices 29(9):140–148. doi:10.1145/185009.185038
  14. Hagemeijer W, Mundkur T (2006) Migratory flyways in Europe, Africa, and Asia and the spread of HPAI H5N1. In: International scientific conference on avian influenza and wild birds, FAO and OIE, Rome, Italy.
  15. Halloran ME, Ferguson NM, Eubank S, Ira M Longini J, Cummings DAT, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P (2008) Modeling targeted layered containment of an influenza pandemic in the US. Proc Natl Acad Sci USA 105(12):4639–4644. doi:10.1073/pnas.0706849105,
  16. Hare M, Deadman PJ (2004) Further towards a taxonomy of agent-based simulation models in environmental management. Mathematics and computers in simulation 64(1):25–40. doi:10.1016/S0378-4754(03)00118-6
  17. Hufnagel L, Brockmann D, Geisel T (2004) Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA 101(42):15124–15129. doi:10.1073/pnas.0308344101,
  18. Kilpatrick AM, Chmura AA, Gibbons DW, Fleischer RC, Marra PP, Daszak P (2006) Predicting the global spread of H5N1 avian influenza. Proc Natl Acad Sci USA103(51):19368–19373. doi:10.1073/pnas.0609227103
  19. Lamport L (1978) Time, clocks, and the ordering of events in a distributed system. Commut ACM 21(7):558–565Google Scholar
  20. LANL (2006) Los Alamos National Laboratory: avian flu modeled on supercomputer, explores vaccine and isolation options for thwarting a pandemic.
  21. Law R, Dieckmann U, Metz JA (2005) The geometry of ecological interactions: simplifying spatial complexity. Cambridge University Press, New YorkGoogle Scholar
  22. Longini IM, Nizam A, Xu S, Ungchusak K, Hanshaoworakul W, Cummunings DAT, Halloran ME (2005) Containing pandemic influenza at the source. Science 309(5737):1083–1087. doi:  10.1126/science.1115717 CrossRefGoogle Scholar
  23. Normile D (2006) Avian influenza: evidence points to migratory birds in H5N1 spread. Science 311(5765):1225. doi: 10.1126/science.311.5765.1225 CrossRefGoogle Scholar
  24. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623. doi: 10.1177/0037549706073695 CrossRefGoogle Scholar
  25. Rao DM, Chernyakhovsky A (2008) Parallel simulation of the global epidemiology of avian influenza. In: Proceedings of the 2008 winter simulation conference, pp 1583–1591Google Scholar
  26. Rao DM, Chernyakhovsk A, Rao V (2007a) SEARUMS: studying epidemiology of avian influenza rapidly using simulation. In: Proceedings of ICMHA’07. Berkeley, pp 667–673Google Scholar
  27. Rao DM, Martin Stieger DDH Jr, Johnson CB, Kidambi P, Narayanan S (2007b) Design and implementation of virtual and constructive simulations using open EAAGLES. Linus Publications, New YorkGoogle Scholar
  28. Rao DM, Chernyakhovsky A, Rao V (2009) Modeling and analysis of global epidemiology of avian influenza. Environ Model Softw 24(1):124–134. doi: 10.1016/j.envsoft.2008.06.011 CrossRefGoogle Scholar
  29. Russell CA, Jones TC, Barr IG, Cox NJ, Garten RJ, Gregory V, Gust ID, Hampson AW, Hay AJ, Hurt AC, de Jong JC, Kelso A, Klimov AI, Kageyama T, Komadina N, Lapedes AS, Lin YP, Mosterin A, Obuchi M, Odagiri T, Osterhaus ADME, Rimmelzwaan GF, Shaw MW, Skepner E, Stohr K, Tashiro M, Fouchier RAM, Smith DJ (2008) The global circulation of seasonal influenza A (H3N2) viruses. Science 320(5874):340–346. doi: 10.1126/science.1154137 CrossRefGoogle Scholar
  30. SEDAC (2007) SocioEconomic Data and Applications Center (SEDAC): gridded population of the world.
  31. Smith GJD, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, MaSK Cheung CL, Raghwani J, Bhatt S, Peiris JSM, Guan Y, Rambaut A (2009) Origins and evolutionary genomics of the 2009 swine-origin h1n1 influenza a epidemic. Nature 459:1122–1125. doi: 10.1038/nature08182 CrossRefGoogle Scholar
  32. Solow AR, Smith WK (2006) Using markov chain successional models backwards. J Appl Ecol 43(1):185188. doi:10.1111/j.1365-2664.2005.01127.x Google Scholar
  33. Tobias R, Hofmann C (2004) Evaluation of free java-libraries for social-scientific agent based simulation. J Artif Soc Soc Simul(JASS) 7(1).
  34. USCB (2006) U.S. Census Bureau: 100 largest counties.
  35. WHO (2005) World Health Organization: ten things you need to know about pandemic influenza.
  36. WHO (2006a) World Health Organization: avian influenza: updates.
  37. WHO (2006b) World Health Organization: H5N1 avian influenza: timeline.
  38. WHO (2006c) World Health Organization (WHO): avian influenza.
  39. WHO (2007a) A description of the process of seasonal and H5N1 influenza vaccine virus selection and development.
  40. WHO (2007b) World Health Organization: avian influenza: situation updates.
  41. Winston WL (1994) Operations research (applications and algorithms), 3rd edn. Duxbury Press, BelmontGoogle Scholar
  42. Wolfram MathWorld (2006) Mercator projection.

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Dhananjai M. Rao
    • 1
  • Alexander Chernyakhovsky
    • 2
  • Victoria Rao
    • 3
  1. 1.CSE DepartmentMiami UniversityOxfordUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Cybernetic EvolutionMasonUSA

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