Table 3. Summary of Simulated Effectiveness of School Closure. Low R0 Higher R0 IR / IR with School Closure IR / IR with School Closure Ferguson 2006 54 / 48 Germann 2006 Glass 2006 Milne 2008 R0 1.7 1.6 1.6 1.5 48 / 1.5 51 / 41 (4) 41 / 16 R0 2.0 1.9 2.0 2.0 68 / 64 65 / 44 75 / 73 (50) 67 / 55 Comparison of School Closure School Closure Assumptions Individual schools close for 3 weeks upon detection of case in school (schools can close multiple times); 10% workplace closure, additional household contact; increased household (50%) and community (25%) contact. Simultaneous and continuous school closure at 10,000 (29 or 24 days) cases plus 7 days; no additional contact. 90% school closure compliance after 10 community cases. School closure infection rate given assuming additional contact and no additional contact in parenthesis. Schools close pre-emptively; additional household contact; adult required to supervise children in household. Simulated effects of school closure on final infection rate for four individual-based influenza epidemic models. For each model results are given for moderate (R#1.7) and severe (R$1.9) epidemics. Each model’s assumptions about the timing of the imposition of school closure and changes in mixing behaviour are summarised. Abbreviations: IR = Infection Rate. doi:10.1371/journal.pone.0004005.t003 contact, compared with household and hub contact; the proportion of asymptomatic infections; and the age-specific attack rate, specifically whether children are more susceptible to infection than adults. Potential impact of non-pharmaceutical interventions We have examined the impact that non-pharmaceutical interventions may have on the course of a pandemic in a systematic manner, investigating a range of interventions in isolation and in combination. Our results suggest that the rapid activation of multiple non-pharmaceutical interventions may have a significant effect on slowing the rate of spread and reducing the final attack rate of an influenza pandemic in a small developed world community. These results hold for all reproduction numbers considered but are more effective for the lower numbers. Our model further suggests that for R0 = 2.5 a local epidemic may be prevented by the use of non-pharmaceutical measures alone, provided that activation of measures is rapid and sustained. While we acknowledge that the long-term enforcement of disruptive non- pharmaceutical interventions is not socially feasible, our results suggest that they have a key role to play, slowing the rate of growth of the pandemic until vaccination or antiviral drugs become available. Furthermore, many countries may not have access to a pandemic vaccine or to antiviral drugs, and if antiviral drugs are used a pandemic virus may rapidly become resistant [27–30], further highlighting the importance of non-pharmaceutical interventions. Comparison of the baseline pandemic and the effect of school closure simulations Several previous studies [7–11] have used individual-based models to simulate the spread of influenza. By comparing the models (including our own), several conclusions can be drawn. Final infection rates, in the absence of any interventions, are similar in all models across a range of R0 values. This is true despite the fact that the models were constructed from a variety of data sources and took different approaches to building the simulated population and its implicit contact network. For epidemics with R0 values greater than 2.0, no single social distancing measure is effective in preventing a local epidemic. For R0 in the range 1.5 to 2.0, different models make quite different predictions about the effectiveness of social distancing measures. The most commonly modelled and clearly comparable intervention is school closure. The effect of school closure as a sole intervention measure shows final infection rates ranging from 1.5% to 48% for R0 in the range 1.5–1.7. Given the similarity in modelled infection rates in the various individual based models in the absence of interventions, the difference in the effect of school closure appears to be related to the differences in assumptions about the contact behaviour of pupils during periods of school closure (an observation also made by Haber et al in [31]). For instance, the study by Germann et al [10] assumed that no additional contact occurred, and found school closure highly effective at R0 = 1.6, with a simulated final infection rate 1.5%. Our study (R0 = 1.5) assumed that additional household contact would occur and found that school closure would be moderately effective, reducing infection rate to 16%. The study by Ferguson et al [8] assumed that both household and community contact would increase and found that school closure for an R0 = 1.7 would only be marginally effective. The Glass et al study [11] supports the hypothesis that simulated school closure effectiveness is related to assumptions about mixing. For an R0 of 1.6, a scenario where pupils continued to contact their friends during school closure found such closure to be marginally effective (reducing infection rate to 41%), while a scenario in which this mixing did not occur resulted in school closure being highly effective (final infection rate 4%). An alternative (or compounding) explanation for the variation in simulated effectiveness of school closure is the different assumptions regarding the timing of its introduction. The Ferguson et al [8] scenario, where individual schools close at the appearance of the first symptomatic case in the school, may result in closure occurring significantly later than the closure scenarios used in the other studies, all of which assume that all schools in a community close before cumulative symptomatic cases in a community reach approximately 10 cases per 10,000. PLoS ONE | www.plosone.org 6 December 2008 | Volume 3 | Issue 12 | e4005