The stochastic modelling study described in [5] found that a comparison of attack rates between adults and children provided a good indication of the likely benefits of closing schools. In our sensitivity analysis (reported in Supporting Information Text S2) we found that an age-specific attack rate profile similar to the 1968 pandemic (where children had much the same attack rate as other age groups, rather than having a higher attack rate) did indeed reduce the effectiveness of school closure. However, we also found that altering assumptions about the size of school class mixing groups or the amount of community contact occurring also resulted in changes to the effectiveness of school closure, even when age-specific attack rates were the same. This apparent lack of consensus highlights the sensitivity of individual-based models to the details of interpersonal contact and individual behavioural patterns, and suggests that obtaining reliable estimates of these parameters should be a priority. All the models suggest that decisions on school closure options, including when and for how long to close schools, will also have a major effect on the final infection rate in a community. The results of our model, and of most of the other individual based models, are applicable to industrialised populations and may not be applicable to developing countries with lower population mobility and higher population densities. However, we have shown that published individual based models of developed world communities estimate similar final attack rates in a pandemic where no interventions have been implemented; that multiple social distancing measures applied early and continuously can be very effective in interrupting transmission of the pandemic virus References 1. Ravchev L, Longini I (1985) A mathematical model for the global spread of influenza. Math Biosci 75: 3–22. 2. Roberts M, Baker M, Jennings L, Sertsou G, Wilson N (2007) A model for the spread and control of pandemic influenza in an isolated geographical region. J Royal Soc Interface 4: 325–330. 3. Colizza V, Barrat A, Barthelemy M, Valleron A, Vespignani A (2007) Modelling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Med 4: e13. 4. Hufnagel L, Brockmann D, Geisel T (2004) Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA 101: 15124–15129. 5. Glass K, Barnes B (2007) How much would closing schools reduce transmission during an influenza pandemic? Epidemiology 18: 623–628. 6. Elveback L, Fox J, Ackerman E, Langworthy A, Boyd M, et al. (1976) An influenza simulation model for immunization studies. Am J Epidemiol 103: 152–165. 7. Ferguson N, Cummings D, Cauchemez S, Fraser C, Riley S, et al. (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437: 209–214. 8. Ferguson N, Cummings D, Fraser C, Cajka J, Cooley P, et al. (2006) Strategies for mitigating an influenza pandemic. Nature 442: 448–452. 9. Longini I, Nizam A, Shufu X, Ungchusak K, Hanshaoworakul W, et al. (2005) Containing pandemic influenza at the source. Science 309: 1083–1087. 10. Germann T, Kadau K, Longini I, Macken C (2006) Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci USA 103: 5935–5940. 11. Glass R, Glass L, Beyeler W, Min H (2007) Targeted social distancing design for pandemic influenza. Emerg Infect Dis 12: 1671–1681. 12. Wu J, Riley S, Fraser C, Leung G (2006) Reducing the impact of the next influenza pandemic using household-based public health interventions. PLoS Med 3: e361. 13. Caley P, Philp D, McCracken K (2007) Quantifying social distancing arising from pandemic influenza. J R Soc Interface 5: 631–639. 14. Bansal S, Pourbohloul B, Meyers L (2006) A comparative analysis of influenza vaccination programs. PLoS Med 3: e387. 15. Keeling M, Rohani P (2002) Estimating spatial coupling in epidemiological systems: a mechanistic approach. Ecol Lett 5: 20–29. 16. May R, Anderson R (1984) Spatial heterogeneity and the design of immunisation programmes. Math Biosci 72: 83–111. 17. Australian Bureau of Statistics (2001) 2001 Census Data by Location. Available: http://www.censusdata.abs.gov.au. Comparison of School Closure for R0 values up to 2.5; and that different conclusions reached on the simulated benefit of school closure probably result from differences in assumptions about the timing and duration of school closure and flow-on effects on other social contacts resulting from school closure. Supporting Information Text S1 Found DOC) Text S2 Found DOC) Additional model details. at: doi:10.1371/journal.pone.0004005.s001 (0.07 Sensitivity analysis at: doi:10.1371/journal.pone.0004005.s002 (0.55 Table S1 Baseline simulation parameters Found at: doi:10.1371/journal.pone.0004005.s003 (0.06 DOC) Acknowledgments The authors thank Professor John Mathews for his input to this work. Author Contributions Conceived and designed the experiments: GJM JK SH. Performed the experiments: JK SH. Analyzed the data: GJM JK HK. Wrote the paper: GJM JK HK SH JM. Provided biological and epidemiological input: HK JM. Sourced demographic data: SH. MB MB MB 18. Gonzalez M, Hidalgo C, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453: 779–782. 19. Papoulis A (1984) Probability, random variables and stochastic processes (2nd edition). New York: McGraw-Hill. 576 p. 20. Monto A, Koopman J, Longini I (1985) Tecumseh study of illness. XIII. Influenza infection and disease, 1976–1981. Am J Epidemiol 121: 811–822. 21. Davis L, Caldwell C, Lynch R, Bailey R, Chin T (1970) Hong Kong influenza: the epidemiologic features of a high school family study analyzed and compared with a similar study during the 1957 Asian influenza epidemic. Am J Epidemiol 92: 240–247. 22. Carrat F, Vergu E, Ferguson N, et al. (2008) Time lines of infection and disease in human influenza: A review of volunteer challenge studies. Am J of Epidemiol 167: 775–785. 23. Longini I, Koopman J, Haber M, Cotsonis G (1988) Statistical inference for infectious diseases: Risk-specific household and community transmission parameters. Am J Epidemiol 128: 845–859. 24. Fox J, Cooney M, Hall C, Foy H (1982) Influenza virus infections in Seattle families, 1975–1979. II. Pattern of infection in invaded households and relation of age and prior antibody to occurrence of infection and related illness. Am J Epidemiol 116: 228–242. 25. Writing Committee of the Second World Health Organization Consultation on Clinical Aspects of Human Infection with Avian Influenza A (H5N1) Virus, Abdel-Nasser A-G, Chotpitayasunondh T, Gao Z, Hayden F, Nguyen D (2008) Update on avian influenza A (H5N1) virus infection in humans. N Engl J Med 358: 261–273. 26. Anderson R, May R (1991) Infectious diseases of humans: Dynamics and control. Oxford: Oxford University Press. 757 p. 27. Ferguson N, Mallett S, Jackson H, Roberts N, Ward P (2003) A population- dynamic model for evaluating the potential spread of drug-resistance influenza virus infections during community-based use of antivirals. J Antimicrob Chemother 51: 977–990. 28. Lipsitch M, Cohen T, Murray M, Levin B (2007) Antiviral resistance and the control of pandemic influenza. PLoS Med 4: e15. 29. de Jong M, Tran T, Truong H, Vo M, Smith G (2005) Oseltamivir resistance during treatment of influenza A (H5N1) infection. N Engl J Med 353: 2667–2672. 30. Le Q, Kiso M, Someya K, Sakai Y, Nguyen T (2005) Avian flu: Isolation of drug-resistant H5N1 virus. Nature 437: 1108. 31. Haber J, Shay D, Davis X, Patel R, Jin X (2007) Effectiveness of interventions to reduce contact rates during a simulated influenza pandemic. Emerg Infect Dis 13: 581–589. PLoS ONE | www.plosone.org 7 December 2008 | Volume 3 | Issue 12 | e4005