Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior



Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak.


Influenza forecasting Responsive behavior Public risk communication Agent-based modeling 



This study was supported by the National Natural Science Foundation of China Grant (71403284) and the Beijing Natural Science Foundation Grant (9162009). The author thanks the editor and three anonymous reviewers for their insightful comments and suggestions.


  1. ADHS (Arizona Department of Health Services) (2009a) Arizona 2009 h1n1 influenza vaccine distribution program 2009–2010 background document. ADHS, Accessed 3 Feb 2015
  2. ADHS (Arizona Department of Health Services) (2009b) Statewide ems pandemic influenza plan. ADHS, Accessed 3 Feb 2015
  3. Balkhy HH, Abolfotouh MA, Al-Hathlool RH, Al-Jumah MA (2010) Awareness, attitudes, and practices related to the swine influenza pandemic among the saudi public. BMC Infect Dis. doi: 10.1186/1471-2334-10-42
  4. Barrat A, Barthelemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  5. Beutels P, Shkedy Z, Aerts M, Van Damme P (2006) Social mixing patterns for transmission models of close contact infections: exploring self-evaluation and diary-based data collection through a web-based interface. Epidemiol Infect 134(6):1158–1166CrossRefGoogle Scholar
  6. Bian L (2004) A conceptual framework for an individual-based spatially explicit epidemiological model. Environ Plann B 31(3):381–395CrossRefGoogle Scholar
  7. Bobashev GV, Goedecke DM, Yu F, Epstein J (2007) A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches. The Society for Computer Simulation International (SCS), Washington, D.CGoogle Scholar
  8. CDC (Centers for Disease Control and Prevention) (2009) Updated interim recommendations for the use of antiviral medications in the treatment and prevention of influenza for the 2009–2010 season. CDC, . Accessed 3 Feb 2015
  9. Coburn BJ, Wagner BG, Blower S (2009) Modeling influenza epidemics and pandemics: insights into the future of swine flu (h1n1). BMC Med. doi: 10.1186/1741-7015-7-30
  10. Cowling BJ, Chan KH, Fang VJ, Cheng CK, Fung RO, Wai W, Sin J, Seto WH, Yung R, Chu DW, Chiu BC, Lee PW, Chiu MC, Lee HC, Uyeki TM, Houck PM, Peiris JS, Leung GM (2009) Facemasks and hand hygiene to prevent influenza transmission in households: a cluster randomized trial. Ann Intern Med 151(7):437–446CrossRefGoogle Scholar
  11. Delre SA, Jager W, Janssen MA (2006) Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Organ Theory 13(2):185–202CrossRefGoogle Scholar
  12. Donaldson LJ, Rutter PD, Ellis BM, Greaves FE, Mytton OT, Pebody RG, Yardley IE (2009) Mortality from pandemic a/h1n1 2009 influenza in England: public health survelliance study. BMJ. doi: 10.1136/bmj.b5213
  13. Donner B (2006) Public warning response to hurricane katrina: a preliminary analysis. Report, Disaster Research CenterGoogle Scholar
  14. Donner WR (2007) An integrated model of risk perception and protective action: public response to tornado warnings. Dissertation, University of DelawareGoogle Scholar
  15. Donner WR, Rodriguez H, Diaz W (2007) Public warning response following tornadoes in new orleans, la, and springfield, mo: a sociological analysis. Paper presented at the Second Symposium on Policy and Socio-economic Research, 87th Annual Meeting of the American Meteorological Society, San AntonioGoogle Scholar
  16. Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, New YorkCrossRefGoogle Scholar
  17. Edmunds WJ, O’Callaghan CJ, Nokes DJ (1997) Who mixes with whom? a method to determine the contact patterns of adults that may lead to the spread of airborne infections. Proc Biol Sci Lond B 264(1384):949–957CrossRefGoogle Scholar
  18. Edmunds WJ, Kafatos G, Wallinga J, Mossong JR (2006) Mixing patterns and the spread of close-contact infectious diseases. Emerg Themes Epidemiol. doi: 10.1186/1742-7622-3-10
  19. Eidelson BM, Lustick I (2004) Vir-pox: an agent-based analysis of smallpox preparedness and response policy. JASSS, Accessed 3 Feb 2015
  20. Ekberg J, Eriksson H, Morin M, Holm E, Stromgren M, Timpka T (2009) Impact of precautionary behaviors during outbreaks of pandemic influenza: modeling of regional differences. AMIA Annu Symp Proc 2009:163–167Google Scholar
  21. Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS (2005) Strategies for containing an emerging infuenza pandemic in Southeast Asia. Nature 437(8):209–214CrossRefGoogle Scholar
  22. Funk S, Bansal S, Bauch CT, Eames KT, Edmunds WJ, Galvani AP, Klepac P (2015) Nine challenges in incorporating the dynamics of behaviour in infectious diseases models. Epidemics 10:21–25CrossRefGoogle Scholar
  23. Galante G, Rizzi RL, Rizzi CB (2015) Simulating epidemiological processes using community-structured scale-free networks. Passo Fundo 7(3):82–96Google Scholar
  24. Grabowskia A, Kosinskia R (2005) The sis model of epidemic spreading in a hierarchical social network. Acta Phys Pol B 36(5):1579–1593Google Scholar
  25. Haber MJ, Shay DK, Davis XM, Patel R, Jin X, Weintraub E, Orenstein E, Thomps WW (2007) Effectiveness of interventions to reduce contact rates during a simulated influenza pandemic. Emerg Infect Dis. doi: 10.3201/eid1304.060828
  26. Huang CY, Sun CT, Lin HC (2005) Influence of local information on social simulations in small-world network models. JASSS, Accessed 7 Sep 2016
  27. Jefferson T, Foxlee R, Mar CD, Dooley L, Ferroni E, Hewak B, Prabhala A, Nair S, Rivetti A (2008) Physical interventions to interrupt or reduce the spread of respiratory viruses: systematic review. BMJ. doi: 10.1136/bmj.39393.510347.BE
  28. Jehn M, Kim Y, Bradley B, Lant T (2011) Community knowledge, risk perception and preparedness for the 2009 influenza a (h1n1) pandemic. J Public Health Man 17(5):431–438Google Scholar
  29. Kamate SK, Agrawal A, Chaudhary H, Singh K, Mishra P, Asawa K (2009) Public knowledge, attitude and behavioural changes in an indian population during the influenza a (h1n1) outbreak. J Infect Dev Ctries 4(1):7–14Google Scholar
  30. Keeling MJ, Eames KT (2005) Networks and epidemic models. J R Soc Interface 2(4):295–307CrossRefGoogle Scholar
  31. Keeling MJ, Rohani P (2008) Modeling infectious disease in humans and animals. Princeton University Press, PrincetonGoogle Scholar
  32. Larson RC, Nigmatulina KR (2010) Engineering responses to pandemics. Stud Health Technol Inform 153:311–339Google Scholar
  33. Lau EH, Griffiths S, Choi KC, Tsui HY (2009) Widespread public misconception in the early phase of the h1n1 influenza epidemic. J Infection 59(2):122–127CrossRefGoogle Scholar
  34. Lau JT, Yang X, Tsui H, Kim JH (2003) Monitoring community psychological responses to the sars epidemic in hong kong: from day 10 to day 62. J Epidemiol Commun H 57(11):864–870CrossRefGoogle Scholar
  35. Lau JT, Kim JH, Tsui HY, Griffiths S (2007) Anticipated and current preventative behaviours in response to an anticipated human-to-human h5n1 epidemic in hong kong chinese general population. BMC Infect Dis. doi: 10.1186/1471-2334-7-18
  36. Lau JT, Griffiths S, Choi KC, Tsui HY (2010) Avoidance behaviors and negative psychological responses in the general population in the initial stage of the h1n1 pandemic in hong kong. BMC Infect Dis. doi: 10.1186/1471-2334-10-139
  37. Lindell MK, Perry RW (2004) Communicating environmental risk in multiethnic communities. Sage, Thousand OaksGoogle Scholar
  38. Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005) Superspreading and the effect of individual variation on disease emergence. Nature 438(7066):355–359CrossRefGoogle Scholar
  39. Mikolajczyk RT, Kretzschmar M (2008) Collecting social contact data in the context of disease transmission: prospective and retrospective study designs. Soc Netw 30(2):127–135CrossRefGoogle Scholar
  40. Mikolajczyk RT, Akmatov MK, Rastin S, Kretzschmar M (2008) Social contacts of school children and the transmission of respiratory-spread pathogens. Epidemiol Infect 136(6):813–822CrossRefGoogle Scholar
  41. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, Massari M, Salmaso S, Tomba GS, Wallinga J, Heijne J, Todys MS, Rosinska M, Edmunds WJ (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLOS Med. doi: 10.1371/journal.pmed.0050074
  42. Newman M (2001) The structure of scientific collaboration networks. PANS 98(2):404–409CrossRefGoogle Scholar
  43. Parker DJ, Priest SJ, Tapsell SM (2009) Understanding and enhancing the public’s behavioural response to flood warning information. Meteorol Appl 16(1):103–114CrossRefGoogle Scholar
  44. Perez L, Dragicevic S (2009) An agent-based approach for modeling dynamics of contagious disease spread. Int J Health Geogr 8(1):50–66CrossRefGoogle Scholar
  45. Perry RW, Lindell MK (2003) Preparedness for emergency response: guidelines for the emergency planning process. Disasters 27(4):336–350CrossRefGoogle Scholar
  46. Philipson TJ (2000) Economic epidemiology and infectious disease. Elsevier, Amsterdam, pp 1761–1799Google Scholar
  47. Philipson TJ, Posner RA (1993) Private choices and public health: the AIDS epidemic in an economic perspective. Harvard University Press, CambridgeGoogle Scholar
  48. Potter GE, Handcock MS, Longini IM, Halloran ME (2012) Estimating within-school contact networks to understand influenza transmission. Ann Appl Stat 6(1):1–26CrossRefGoogle Scholar
  49. Quarantelli EL (1983) People’s reactions to emergency warnings. Report, Disaster Research CenterGoogle Scholar
  50. Quarantelli EL (1990) The warning process and evacuation behavior: the research evidence. Report, Disaster Research CenterGoogle Scholar
  51. Read JM, Eames KT, Edmunds WJ (2008) Dynamic social networks and the implications for the spread of infectious disease. J R Soc Interface 5:1001–1007CrossRefGoogle Scholar
  52. Roberts SG, Dunbar RI, Pollet TV, Kuppens T (2009) Exploring variation in active network size: constraints and ego characteristics. Soc Netw 31(2):138–146CrossRefGoogle Scholar
  53. Rubin GJ, Amlot R, Page L, Wessely S (2009) Public perceptions, anxiety, and behaviour change in relation to the swine flu outbreak: cross sectional telephone survey. BMJ. doi: 10.1136/bmj.b2651
  54. Salathe M, Jones JH (2010) Dynamics and control of diseases in networks with community structure. PLOS Comput Biol. doi: 10.1371/journal.pcbi.1000736
  55. Salathe M, Kazandjievab M, Leeb JW, Levisb P, Feldmana MW, Jonesc JH (2010) A high-resolution human contact network for infectious disease transmission. PNAS 107(51):22,020–22,025Google Scholar
  56. Tang CS, Cy Wong (2003) An outbreak of the severe acute respiratory syndrome: predictors of health behaviors and effect of community prevention measures in Hong Kong, China. Am J Public Health 93(11):1887–1888CrossRefGoogle Scholar
  57. Tang CS, Wong CY (2004) Factors influencing the wearing of facemasks to prevent the severe acute respiratory syndrome among adult chinese in Hong Kong. Prev Med 39(6):1187–1193CrossRefGoogle Scholar
  58. Taylor M, Raphael B, Barr M, Agho K, Stevens G, Jorm L (2009) Public health measures during an anticipated influenza pandemic: factors influencing willingness to comply. Risk Manag Healthc Policy 2:9–20CrossRefGoogle Scholar
  59. Tuite AR, Greer AL, Whelan M, Winter AL, Lee B, Yan P, Wu J, Moghadas S, Buckeridge D, Pourbohloul B, Fisman DN (2010) Estimated epidemiologic parameters and morbidity associated with pandemic h1n1 influenza. CMAJ 182(2):131–136CrossRefGoogle Scholar
  60. Vaughan E, Tinker T (2009) Effective health risk communication about pandemic influenza for vulnerable populations. Am J Public Health 99(s2):S324–S332CrossRefGoogle Scholar
  61. Watts DJ (1999) Networks, dynamics, and the small-world phenomenon. Am J Sociol 105(2):493–527CrossRefGoogle Scholar
  62. WHO (World Health Organization) (2003) Consensus document on the epidemiology of severe acute respiratory syndroms (sars). WHO, Accessed 3 Feb 2015
  63. Wu JT, Cowling BJ, Lau EH, Ip DK, Ho LM, Tsang T, Chuang SK, Leung PY, Lo SV, Liu SH, Riley S (2010) School closure and mitigation of pandemic (h1n1) 2009, Hong Kong. Emerg Infect Dis 16(3):538–541CrossRefGoogle Scholar
  64. Wu KM, Riley S (2016) Estimation of the basic reproductive number and mean serial interval of a novel pathogen in a small, well-observed discrete population. PLoS ONE. doi: 10.1371/journal.pone.0148061
  65. Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL, Matrajt L, Potter G, Kenah E, Longini IM (2009) The transmissibility and control of pandemic influenza a (h1n1) virus. Science 326(5953):729–733CrossRefGoogle Scholar
  66. Yoo BK, Kasajima M, Bhattacharya J (2010) Public avoidance and the epidemiology of novel h1n1 influenza a. Report, National Bureau of Economic ResearchGoogle Scholar
  67. Zhong W, Kim Y, Jehn M (2013) Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 h1n1 outbreak in Arizona. Comput Math Organ Theory 19(4):622–645CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Public Administration and PolicyRenmin University of ChinaBeijingChina

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