recurrent pattern with a 24-hour period [18], indicating that our assumption that individuals occupy two primary locations (a household and a hub) is a reasonable abstraction of human mobility in the developed world. Additional information about the population model construction and simulation algorithm can be found in Supplementary Information Text S1. In addition to household and hub contact, individuals in the simulation also engaged in random or untraceable community contacts. This contact was assumed to be local in nature, with contacts between individuals from the same or nearby areas being relatively more likely than contact with individuals from distant patches. When an infectious and susceptible individual came into contact during a simulation cycle, the probability that the infection was transmitted was calculated according to a transmission function (see below). For each contact event, an infection state (either to remain susceptible or to become infected) for the susceptible individual was randomly chosen via a Bernoulli trial [19]. The transmission probability Ptrans for a contact event is a function of the states of the infectious (Ii) and susceptible (Is) individuals involved: PtransðIi, IsÞ~b|inf ðIiÞ|suscðIsÞ where b is the basic transmission coefficient, initially chosen to give an epidemic with a final attack rate consistent with seasonal influenza. To achieve simulations under a range of reproduction numbers, b was increased from the baseline value to achieve epidemics with target R0 values of 1.5, 2.0 and 2.5 (see Supplementary Info Table S1.1). R0 was derived by inserting an infectious individual randomly into a totally susceptible popula- tion, counting the number of resulting secondary infections, and averaging over 10,000 such trials. This is also the method used to derive R0 in [7–10]; additional information may be found in Supplementary Info Text S1. The infectivity parameter inf(Ii) was set to 1 for symptomatic individuals, and 0.5 for infectious but asymptomatic individuals. The susceptibility parameter susc(Is) is a function directly dependent on the susceptible person’s age. It captures the age- varying susceptibility to transmission, due to both partial prior immunity and age-related differences in contact behaviour. To achieve a realistic age specific infection rate, the age-specific susceptibility parameters were calibrated against the serologic infection rates reported for H3N2 in 1977–1978 in Tecumseh, Michigan [20] (see Supplementary Information Table S1.2). We included in our sensitivity analyses an alternative calibration of age-specific susceptibilities that gave rise to a flat age-specific attack rate, similar to that of the 1968 pandemic [21] (see Supporting Information Text S2). Influenza infection was modelled to last 6 days: 1 day latent, 1 day asymptomatic and infectious and 4 days infectious (either symptomatic or asymptomatic). We also assumed constant infectivity for the infectious period, which is a simplification of the presumed infectivity distribution found in studies of viral shedding [21,22]. For baseline (no-intervention) epidemics with R0 values of 1.5, 2.0 and 2.5 these timing parameters gave serial intervals of 2.97, 2.87, 2.74 days respectively, which are consistent with previous estimates for pandemic and seasonal influenza [7,13,23]. Infected individuals were assumed to be immune to re-infection for the duration of the simulation. We also assumed that influenza symptoms developed 48 hours after infection with 20% of infections being asymptomatic in people aged 18 years or less and 32% being asymptomatic among older adults. These percentages were derived by summing the age-specific antibody Comparison of School Closure titres determined in Table 5 of [24]. Symptomatic individuals were modelled to withdraw into the home with probability 50% for adults and 90% for children (ages 6–17). We have used ‘‘illness attack rate’’ or ‘‘attack rate’’ to mean the proportion of the population who experience symptomatic infection, while ‘‘infection rate’’ refers to the proportion who were infected with symptomatic or asymptomatic infection. Application of the model to pandemic influenza We assumed similar viral characteristics for pandemic influenza as we developed for seasonal influenza, as is suggested by existing data for H5N1 [25]. We further assumed a pandemic had been declared in South-East Asia, that the pandemic virus was already thought to be in Australia and that our modelled population was aware of the likely arrival of the pandemic strain. We therefore assumed a level of spontaneous social distancing in response to public health announcements and news reports. Such pandemic behaviour was assumed to contrast with that which occurs with seasonal influenza where overall mobility and contact patterns of asymptomatic individuals, and even some who are ill and symptomatic, remain unaltered. Baseline parameters were chosen to give rise to an epidemic with the following characteristics: an R0 of 1.5 giving a final illness attack rate of 34%, with 43% of infections occurring in households, 29% in schools and workplac- es, and 26% from community contact. Based on seasonal influenza data [20] it has been estimated that 33%–37% of transmission occurs in the household [8]. Given that public knowledge of a current pandemic would induce spontaneous social distancing, we believe it reasonable to assume a lower level of community, school and workplace contact and a higher proportion of household transmission (43% rather than approximately 35%). Modelling interventions in a pandemic We simulated four different non-pharmaceutical intervention measures as follows. School closure. We assumed that when schools were closed, students and teachers spent weekday daytime cycles at home rather than at school. This meant that no contact took place at that school hub, but that these individuals would contact any other individuals present in their household during the day cycle. We assumed that no additional community contact occurred (community contact was deemed to occur in all daytime cycles for active individuals, regardless of whether they were present at a hub or home). We also assumed that if school closure would result in a child being present in a household alone, one adult from the household stayed home (and did not make hub contacts). We assumed school closure applied to childcare facilities, all schools, and all adult educational institutions. Increased case isolation. Our baseline assumption was that upon becoming symptomatic, there was a 50% chance that an adult, and 90% chance that a child, would withdraw to their household for the duration of their infection (infectivity and symptoms were deemed to cease at the same time). When the increased case isolation intervention was in effect, this increased house withdrawal to 90% for adults and 100% for children (ages 6–17). We assumed that withdrawn individuals made only household contacts while withdrawn. Workplace non-attendance. When this measure was in effect, each person attending a (non-school) workplace hub had a 50% chance each day of staying home instead of attending the hub (the choice was made independently each day and applied only that day). Individuals staying at home made no hub contacts but did contact all other individuals also at home during the day cycle. PLoS ONE | www.plosone.org 3 December 2008 | Volume 3 | Issue 12 | e4005