Advertisement

Modeling the Impact of Behavior Changes on the Spread of Pandemic Influenza

  • Sara Y. Del ValleEmail author
  • Susan M. Mniszewski
  • James M. Hyman
Chapter

Abstract

We use mathematical models to assess the impact of behavioral changes in response to an emerging epidemic. Evaluating the quantitative and qualitative impact of public health interventions on the spread of infectious diseases is a crucial public health objective. The recent avian influenza (H5N1) outbreaks and the 2009 H1N1 pandemic have raised significant global concerns about the emergence of a deadly influenza virus causing a pandemic of catastrophic proportions. Mitigation strategies based on behavior changes are some of the only options available in the early stages of an emerging epidemic when vaccines are unlikely to be available and there are only limited stockpiles of antiviral medications. Mathematical models that capture these behavior changes can quantify the relative impact of different mitigation strategies, such as closing schools, in slowing the spread of an infectious disease. Including behavior changes in mathematical models increases complexity and is often left out of the analysis. We present a simple differential equation model which allows for people changing their behavior to decrease their probability of infection. We also describe a large-scale agent-based model that can be used to analyze the impact of isolation scenarios such as school closures and fear-based home isolation during a pandemic. The agent-based model captures realistic individual-level mixing patterns and coordinated reactive changes in human behavior in order to better predict the transmission dynamics of an epidemic. Both models confirm that changes in behavior can be effective in reducing the spread of disease. For example, our model predicts that if school closures are implemented for the duration of the pandemic, the clinical attack rate could be reduced by more than 50%. We also verify that when interventions are stopped too soon, a second wave of infection can occur.

Keywords

Attack Rate Pandemic Influenza Disease Spread School Closure Standard Industry Classification 
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.

Notes

Acknowledgements

We would like to acknowledge the Institutional Computing Program at Los Alamos National Laboratory for use of their HPC cluster resources. This research has been supported at Los Alamos National Laboratory under the Department of Energy contract DE-AC52-06NA25396 and a grant from NIH/NIGMS in the Models of Infectious Disease Agent Study (MIDAS) program (U01-GM097658-01).

References

  1. 1.
    Ackerman, E., Elveback, L.R., Fox, J.P.: Simulation of Infectious Disease Epidemics. CC Thomas, Springfield, Illinois (1988)Google Scholar
  2. 2.
    Anderson, R.M., May, R.M.: Infectious Diseases of Humans. Oxford University Press, Oxford (1991)Google Scholar
  3. 3.
    Bansal, S., Pourbohloul, B., Meyers, L.A.: PLoS Med. e387 (2006)Google Scholar
  4. 4.
    Bansal, S., Grenfell, B.T., Meyers, L.A.: J. R. Soc. Interface 4, 879 (2007)CrossRefGoogle Scholar
  5. 5.
    Blower, S.M., Porco, T.C., Darby, G.: Nature Med. 4, 673 (1998). doi: 10.1038/nm0698-673Google Scholar
  6. 6.
    Bootsma, M.C.J., Ferguson, N.M.: Proc. Natl. Acad. Sci. USA 104, 7588 (2007). doi: 10.1073/pnas.0611071104Google Scholar
  7. 7.
    Caley, P., Philp, D.J., McCracken, K.: J. R. Soc. Interface 6, 631 (2008). doi: 10.1098/rsif.2007.1197CrossRefGoogle Scholar
  8. 8.
    Cauchemez, S., Valleron, A.J., Boëelle, P.Y., Flahault, A., Ferguson, N.M.: Nature 452, 750 (2008). doi: 10.1038/nature06732CrossRefGoogle Scholar
  9. 9.
    Centers for Disease Control and Prevention (CDC). Interim pre-pandemic planning guidance: Community strategy for pandemic influenza mitigation in the United States Early, targeted, layered use of nonpharmaceutical interventions. http://www.flu.gov/planning-preparedness/community/community_mitigation.pdf. Cited 4 Apr 2012
  10. 10.
    Chowell, G., Nishiura, H.: Phys. Life Rev. 5, 50 (2008). doi: 10.1016/j.plrev.2007.12.001CrossRefGoogle Scholar
  11. 11.
    Chowell, G., Hyman, J.M., Eubank, S., Castillo-Chavez, C.: Phys. Rev. E 68(6 Pt 2), 066102 (2003). doi: 10.1103/PhysRevE.68.066102CrossRefGoogle Scholar
  12. 12.
    Chowell, G., Ammon, C.E., Hengartner, N.W., Hyman, J.M.: J. Theor. Biol. 241, 193 (2006). doi: 10.1016/j.jtbi.2005.11.026MathSciNetCrossRefGoogle Scholar
  13. 13.
    Colizza, V., Barrat, A., Barthelemy, M., Valleron, A., Vespignani, A.: PLoS Med. 4, e13 (2007). doi: 10.1371/journal.pmed.0040013CrossRefGoogle Scholar
  14. 14.
    Cooper, B.S., Pitman, R.J., Edmunds, W.J., Gay, N.J.: PLoS Med. 3, e212 (2006). doi: 10.1371/journal.pmed.0030212Google Scholar
  15. 15.
    Del Valle, S., Hethcote, H., Hyman, J.M., Castillo-Chavez, C.: Math. Biosc. 195, 228 (2005). doi: 10.1016/j.mbs.2005.03.006zbMATHCrossRefGoogle Scholar
  16. 16.
    Del Valle, S.Y., Hyman, J.M., Hethcote, H.W., Eugank, S.G.: Soc. Networks 29, 539 (2007). doi: 10.1016/j.socnet.2007.04.005Google Scholar
  17. 17.
    Edmunds, W.J., O’ Calaghan, C.J., Nokes, D.J.: Proc. R. Soc. B 22, 264 (1997). doi: 10.1098/rspb.1997.0131Google Scholar
  18. 18.
    Eubank, S.G., Guclu, H., Kumar, V.A., Marathe, M.V., Srinivasan, A., Toroczkai, Z., Wang, N.: Nature 429, 180 (2004). doi: 10.1038/nature02541CrossRefGoogle Scholar
  19. 19.
    Ferguson, N.: Nature 446, 733 (2007). doi: 10.1038/446733aCrossRefGoogle Scholar
  20. 20.
    Ferguson, N.M., Keeling, M.J., Edmunds, W.J., Gani, R., Grenfell, B.T., Anderson, R.M., Leach, S.: Nature 425, 681 (2003). doi:10.1038/nature02007CrossRefGoogle Scholar
  21. 21.
    Ferguson, N.M., Cummings, D.A., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A., Iamsirithaworn, S., Burke, D.S.: Nature 437, 209 (2005). doi: 10.1145/1315843.1315857CrossRefGoogle Scholar
  22. 22.
    Fukś, H., Lawniczak, A.T., Duchesne, R.: Eur. Phys. J. B 50, 209 (2006). doi: 10.1140/epjb/e2006-00136-7CrossRefGoogle Scholar
  23. 23.
    Gani, R., Hughes, H., Fleming, D., Griffin, T., Medlock, J., Leach, S.: Infect. Dis. 11, 1355–1362 (2005)Google Scholar
  24. 24.
    Germann, T.C., Kadau, K., Longini, I.M. Jr., Macken, C.A.: Proc. Natl. Acad. Sci. USA 103, 5935 (2006). doi: 10.1073/pnas.0601266103Google Scholar
  25. 25.
    Glass, K., Barnes, B.: Epidemiology 18, 623 (2007). doi: 10.1097/EDE.0b013e31812713b4CrossRefGoogle Scholar
  26. 26.
    Handel, A., Longini, I.M., Antia, R.: Proc. R. Soc. B 274, 833 (2007). doi: 10.1098/rspb.2006.0015Google Scholar
  27. 27.
    Hatchett, R.J., Mecher, C.E., Lipsitch, M.: Proc. Natl. Acad. Sci. USA 104, 7582 (2007). doi: 10.1073/pnas.0610941104Google Scholar
  28. 28.
    Hethcote, H.W.: SIAM Rev. 42, 599 (2000). 10.1137/S0036144500371907Google Scholar
  29. 29.
    Isham, V., Mdley, G.: Models for Infectious Human Diseases: Their Structure and Relation to Data. Cambridge University Press, Cambridge (1996)zbMATHCrossRefGoogle Scholar
  30. 30.
    Johnson, N.P., Mueller, J.: Bull. Hist. Med. 76, 105 (2002). doi: 10.1353/bhm.2002.0022CrossRefGoogle Scholar
  31. 31.
    Kermack, W.O., McKendrick, A.G.: Proc. R. Soc. Series A 115, 700 (1927). 10.1098/rspa.1927.0118Google Scholar
  32. 32.
    Kilbourne, E.D.: Emerg. Infec. Dis. 12, 9 (2006). doi: 10.3201/eid1201.051254CrossRefGoogle Scholar
  33. 33.
    Lin, J., Zhang, J., Dong, X., Fang, H., Chen, J., Su, N., Gao, Q., Zhang, Z., Liu, Y., Wang, Z., Yang, M., Sun, R., Li, C., Lin, S., Ji, M., Liu, Y., Wang, X., John, W., Feng, Z., Wang, Y., Yin, W.: Lancet 368, 991 (2006). doi: 10.1016/S0140-6736(06)69294-5CrossRefGoogle Scholar
  34. 34.
    Longini, I.M., Halloran, M.E., Nizam, A., Yang, Y.: Am. J. Epidemiol. 159, 623 (2004). doi: 10.1093/aje/kwh092CrossRefGoogle Scholar
  35. 35.
    Meyers, L.A., Pourbohloul, B., Newman, M.E.J., Skowronski, D.M., Brunham, R.C.: J. Theor. Biol. 232, 71 (2004). doi: 10.1016/j.jtbi.2004.07.026MathSciNetCrossRefGoogle Scholar
  36. 36.
    Michaels, J.: Commercial buildings energy consumption survey. (2003). http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables\_2003.html. Cited 12 June 2012
  37. 37.
    National Household Travel Survey (NHTS). http://www.bts.gov/programs/national-household-travel-survey. Cited 4 April 2012
  38. 38.
    Neuzil, K.M., Hohlbein, C., Zhu, Y.: Arch. Pediar. Adolesc. Med. 156, 986 (2002)Google Scholar
  39. 39.
    Newman, M.E.J.: Phys. Rev. E 66, 016128 (2002). doi: 10.1103/PhysRevE.66.016128Google Scholar
  40. 40.
    Nuno, M., Chowell, G., Gumel, A.: J. R. Soc. Interface 22, 505 (2007). doi: 10.1098/?rsif.2006.0186Google Scholar
  41. 41.
    Ofner-Agostini, M., Wallington, T., Henry, B., Low, D., McDonald, L.C., Berger, L., Mederski, B., SARS Investigative Team, Wong, T.: Investigation of the second wave (phase 2) of severe acute respiratory syndrome (SARS) in Toronto, Canada. What happened? Can. Commun. Dis. Rep. 34, 1–11 (2008)Google Scholar
  42. 42.
    Pang, X., Zhu, Z., Xu, F., Guo, J., Gong, X., Liu, D., Liu, Z., Chin, D.P., Feikin, D.R.: J. Am. Med. Assoc. 290, 3215 (2003)CrossRefGoogle Scholar
  43. 43.
    Reed, J.M., Keeling, M.J.: Proc. R. Soc. B 270, 699 (2003). doi: 10.1098/rspb.2002.2305Google Scholar
  44. 44.
    Ross, R.: The Prevention of Malaria. John Murray, London (1911)Google Scholar
  45. 45.
    Smith, D.J., Forrest, S., Ackley, D.H., Perelson, A.S.: Proc. Natl. Acad. Sci. USA 96, 14001 (1999). doi: 10.1073/pnas.96.24.14001Google Scholar
  46. 46.
    Stilianakis, N.I., Perelson, A.S., Hayden F.G.: J. Infect. Dis. 177, 863 (1998). doi: 10.1086/515246CrossRefGoogle Scholar
  47. 47.
    Stohr, K., Esveld, M.: Science 306, 2195 (2004). doi: 10.1126/science.1108165CrossRefGoogle Scholar
  48. 48.
    Stroud, P.D., Del Valle, S.Y., Sydoriak, S.J., Riese, J., Mniszewski, S.: J. Artif. Soc. Soc. Simulat. 10(4), 9 (2007)Google Scholar
  49. 49.
    US Homeland Security Council (HSC), National Strategy for Pandemic Influenza Implementation Plan. http://www.flu.gov/planning-preparedness/federal/pandemic-influenza-oneyear.pdf. Cited 4 Apr 2012
  50. 50.
    United Nations (UN), Department of Economic and Social Affairs, Population Division, Urban Population Development and the Environment (2007), http://www.un.org/esa/population/publications/2007-PopDevt/2007-PopDevt-Urban.htm. Cited 4 Apr 2012
  51. 51.
    Wallinga, J., Teunis, P., Kretzschmar, M.: Am. J. Epidemiol. 164, 936 (2006). doi: 10.1093/aje/kwj317CrossRefGoogle Scholar
  52. 52.
    Woodson, G.: 2005 Preparing for the Coming Influenza Pandemic. http://earthsky.org/health/grattan-woodson-interview Cited 4 Apr 2012
  53. 53.
    Yee, D., Bradford, J.: Employment density study, Canadian METRO Council Technical Report (1999)Google Scholar
  54. 54.
    Zaric, G.S.: Healthcare Manage. Sci. 5, 147 (2002). doi: 10.1023/A:1014489218178Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sara Y. Del Valle
    • 1
    Email author
  • Susan M. Mniszewski
    • 1
  • James M. Hyman
    • 2
  1. 1.Los Alamos National LaboratoryLos AlamosUSA
  2. 2.Tulane UniversityNew OrleansUSA

Personalised recommendations