Intervention scenario Baseline School Closure Case Isolation Workplace Nonattendance Community Contact Reduction School Closure+Case Isolation School Closure+Workplace Nonattendance School Closure+Community Contact Reduction 3 All Measures R0 = 1.5 Final attack rate % 33 13 6 24 16 3 6 2 89 20 9.0 54 25 4.0 10 5.0 3.0 R0 = 2.0 Final attack rate % 55 45 30 48 41 8 34 12 2 279 146 78 210 142. 12 80 17 4 Table 2. Simulated final and peak daily attack rates for epidemics with non-pharmaceutical interventions. Peak daily attack rate (cases per 10000) Peak daily attack rate (cases per 10000) Peak daily attack rate (cases per 10000) Comparison of School Closure R0 = 2.5 Final attack rate % 65 60 49 60 55 30 54 36 3 474 321 221 389 291 67 25 89 5 Final attack rates and peak daily attack rates are given as percentages of the population, for epidemics with baseline R0 values of 1.5, 2.0 and 2.5. For each measure or combination of measures, results are given for optimal application (pre-emptive activation and indefinite duration). All results are means of 40 independent randomly seeded simulation runs. doi:10.1371/journal.pone.0004005.t002 is similar to Figure 2 in the work of Roberts et al [2]. Both figures plot final infection rate against R0 for a number of individual- based models. Our figure adds additional data points for the Ferguson et al 2006 study [8] and includes two additional models: the Glass et al 2007 study [11] and the study described in this paper. School closure was the most widely represented non-pharma- ceutical intervention in comparable simulation studies. Three previous studies [8,10,11] simulated school closure as a sole intervention measure. Table 3 compares the effect of school closure for these studies and our own. The effect of school closure on final infection rates is shown, and for each study assumptions about the operation of school closure are summarised. Most models concluded that school closure would have only a modest effect when R0 was approximately 2.0. Ferguson et al [8] modelled school closure occurring for periods of 3 weeks triggered by the appearance of a case within the school, and found a 4% decrease in the infection rate (68% to 64%). The effect on the infection rate was more dramatic in the model of Germann et al, decreasing from 65% to 44%, but this model assumed that school closure caused no additional increase in interpersonal contact [10]. Early and continuous school closure in the model of Glass et al resulted in a decrease in the infection rate from 73% to 50% [11]. Our model, also assuming early and continuous school closure, resulted in a decrease in infection rate from 67% to 55%. Discussion The scale of our model (approximately 30,000 individuals) is both small enough to allow the collection of detailed information and large enough to encompass the level at which public health planning and response might take place. The model of Albany was constructed using a large number of parameters and is sensitive to the values assigned to them. We have utilised data from studies of past pandemics, from seasonal influenza epidemics and from related modelling work to set parameters but some parameters remain difficult to estimate. Sensitivity analyses, reported in Supporting Information Text S2, suggest that the model is most sensitive to assumptions regarding mixing group sizes in schools; the relative number of community Figure 2. Simulated final infection rates plotted against basic reproduction number R0 for a number of epidemic models, assuming no intervention. doi:10.1371/journal.pone.0004005.g002 PLoS ONE | www.plosone.org 5 December 2008 | Volume 3 | Issue 12 | e4005