spreading into the community from other parts of the state. We explored social distancing measures that included school (and child care) closure, reduced workplace attendance, reduced social and community contact, and increased home isolation of symptomatic individuals. These measures were examined for epidemics with basic reproduction numbers of 1.5, 2.0 and 2.5. The basic reproduction number (R0) is the average number of secondary cases that would be infected by a single infectious individual in a totally susceptible population, and is a measure of the transmis- sibility of an infectious disease. Since the R0 value of a new pandemic strain of influenza is unknown, we covered a range of R0 estimates for previous pandemics [7,8,13]. Furthermore we compared the baseline outputs of our model and the effects of a specific social distancing measure, school closure, with the results from other individual-based models. Methods Population model construction We constructed a geographic and demographic model of Albany, Western Australia using a location-based connected spatial structure [3,14–16]. Australian Bureau of Statistics Census Collection Districts (CCD) were the finest level of spatial detail used, with each CCD consisting of approximately 200 physically adjacent households. Each such area was populated with a number of households according to the 2001 census data [17]; the constituent households each being uniquely populated with individuals whose specific ages matched the demographics and household age-structure of each CCD. The model was further populated with a set of schools and workplaces, referred to collectively as contact hubs. Data from the state government of Western Australia were used to obtain a comprehensive list of schools, childcare facilities, adult education institutions (provided by the Department of Education and Training; Frankland D, personal communication) and employers, Comparison of School Closure including the location in which they were located and their nominal daytime population (provided by the Department of Planning and Infrastructure; Piscicelli A, personal communica- tion). Each child was assigned to a school or childcare centre, presuming that children attended a school as close to their home location as possible, and ensuring that the known age structure of schools was maintained. Adult students and workers were assigned to adult education institutions and workplaces respectively, with this assignment being made with reference to census journey-to- work information survey data for Western Australia (provided by the Department of Planning and Infrastructure; Pradzynski J, personal communication). Application of the model to influenza infection Using this population model, we conducted stochastic, individ- ual-based spatial simulations of an influenza epidemic, assuming that an average of one new infection per day was introduced into the population for the duration of the simulation. Each simulation proceeded in a sequence of 12 hour day/night cycles. During each cycle the nominal location of each individual was decided: either household or hub, taking into account the cycle type (i.e. day/ night, weekend/weekday), the individual’s infection status and whether an individual needed to stay at home to supervise a child. During each cycle, individuals occupying the same location were deemed to come into potential infective contact when infection transmission could occur. For larger hubs, including schools, we assumed it was unlikely that an individual would come into close contact with every other member of the hub during a cycle. These larger hubs were therefore divided into fixed mixing groups, with a maximum size of 10 individuals per group. In schools these mixing groups consisted of same-aged children where possible; in workplaces they were randomly assigned. The overlapping memberships of households and hubs formed a connected social network (see Figure 1). A study using mobile telephone location data has shown that human movement is dominated by a Figure 1. Idealised household and hub contact network. doi:10.1371/journal.pone.0004005.g001 PLoS ONE | www.plosone.org 2 December 2008 | Volume 3 | Issue 12 | e4005