Community contact reduction. When this measure was in effect, it was assumed that individuals participating in community contact during a simulation cycle made 50% of the baseline number of effective contacts. The degree of compliance to any intervention measure will obviously influence the effectiveness of the measure. For our simulations we have chosen parameters that, while severe, may be plausible in the context of a pandemic with significant mortality. Results of simulations examining the relationship between the degree of compliance and the derived effectiveness of each intervention are reported in Text S2. Comparison with other individual based studies We reviewed the published results from five individual based simulation studies of the developing and developed world settings [7–11] and compared final infection rates from these studies and the effect of simulated school closure with the corresponding results from our own modelling. In some cases, symptomatic attack rates were reported; we converted these to infection rates using the asymptomatic infection proportion used by that study. Additional information about this aspect of the study can be found in Supporting Information Text S1. All numerical model parameters are listed in Supporting Information Table S1. Results Pandemic characteristics with no interventions We conducted baseline simulations (assuming no intervention measures) for epidemics with R0 values of 1.5, 2.0 and 2.5 As our simulations are stochastic in nature, the outcome of a simulated epidemic for a fixed set of model parameters varied depending, for example, on the choice of individuals who were ‘‘seeded’’ as infectious index cases; the outcome of possibly infectious contact; and the probabilistic contact behaviour of individuals. All results were averages of 40 independent simulation runs made with different random number seeding sequences. As we assumed a continuous influx of infectious cases from outside the simulation boundary at a rate of one per day, we achieve a sustained epidemic for every simulation. Final attack rates ranged from 33% to 65% corresponding to R0 values of 1.5 and 2.5, while peak daily attack rates ranged from 89 to 474 cases per 10,000 (Table 1). Modelling social distancing interventions We simulated the effects of four different non-pharmaceutical interventions: school closure, increased voluntary isolation of symptomatic individuals, workplace non-attendance and reduced Comparison of School Closure community contact, with assumptions about each intervention as described above. We simulated the optimal application timing of the measures by assuming that the interventions were implement- ed prior to the introduction of the first infected case and continued indefinitely. In Table 2 we present figures capturing the cumulative and daily attack rates determined by simulated epidemics with (unmitigated) R0 values of 1.5, 2.0 and 2.5. We also conducted a series of simulations to determine the sensitivity of intervention measures to variation in key model parameters, including the degree of compliance, with results presented in the Supporting Information Text S2. For epidemics with an unmitigated R0 of 1.5, case isolation, school closure or community contact reduction made significant reductions in the final attack rate, reducing it from 33% to 6%, 13% and 16% respectively. School closure combined with any of the other interventions reduced the cumulative attack rate to below 10%, which may be deemed to be the threshold below which an epidemic does not occur. School closure combined with case isolation reduced the final attack rate to 8%. For epidemics with an R0 of 2.5, only the combination of all the modelled intervention measures appeared capable of controlling the epidemic, reducing the final attack rate from 65% to 3%. School closure combined with case isolation more than halved the final attack rate (to 30%). To achieve the large reductions in attack rates for R0 values of 2.0 and 2.5, combinations of interventions needed to operate for unfeasibly long periods of time (greater than 5 months). The effect of interventions on peak daily attack rates followed a similar pattern to that of final attack rates, although the proportional reductions resulting from each intervention was larger than for final attack rates. Comparison with other studies The characteristics of our baseline epidemics were consistent with other simulations based on stochastic individual-based models, specifically [7–11]. Figure 2 shows predicted final infection rates plotted against basic reproduction number R0 for five such models: orange (Ferguson et al 2005, Thailand) [7], light green (Ferguson et al 2006, United Kingdom) [8], brown (Longini et al 2005, Thailand) [9], dark green (Germann et al 2006, U.S.A.) [10], and light blue (Glass et al 2007, a synthetic 10,000 member community) [11]. Dark blue represents this study. The final infection rate predicted by a simple susceptible-infected-removed (SIR) differential equation model assuming uniform [26] is included for comparison (red). It should be noted that Figure 2 Table 1. Simulated outcome of baseline (no-intervention) epidemics for three R0 values. Final infection rate (%) Final illness attack rate (%) Peak symptomatic population (%) Peak daily attack rate (cases per 10000) Peak attack day Serial interval (days) R0 = 1.5 mean 39.6 33.2 5.3 89 58 2.97 (95% CI) (60.5) (60.4) (60.17) (63.0) (62.3) (60.005) R0 = 2.0 mean 66.7 54.9 17.1 279 37 2.87 (95% CI) (60.2) (60.2) (60.17) (63.6) (61.0) (60.004) R0 = 2.5 mean 79.6 64.8 28.3 474 28 2.74 (95% CI) (60.1) (60.1) (60.17) (65.8) (60.7) (60.003) Model parameters for the R0 = 1.5 epidemic were determined as described in the text. The fundamental transmission probability b was increased to give epidemics with measured R0 values of 1.5, 2.0 and 2.5. The statistics given for each baseline epidemic are means of 40 independent randomly seeded simulation runs (95% confidence intervals for the 40-run means are given in parentheses). doi:10.1371/journal.pone.0004005.t001 PLoS ONE | www.plosone.org 4 December 2008 | Volume 3 | Issue 12 | e4005