Mao BMC Public Health 2011, 11:522 http://www.biomedcentral.com/1471-2458/11/522 Page 3 of 10 Table 1 Design of six weekend-extension strategies and their abbreviations Patterns Lengths (day) One Two Three Continuous Monday (Mon) Monday + Tuesday (Mon + Tue) Monday +Tuesday + Wednesday (Mon+Tue+Wed) Discontinuous Wednesday (Wed) Tuesday + Thursday (Tue + Thur) Monday + Wednesday + Friday (Mon+Wed+Fri) population, according to the travel time to work and industrial types of business locations. Next, the pastime population was generated based on the information of previous two populations and a regional travel survey [16]. Under the constraints of travel statistics, indivi- duals were further allocated to service places and neigh- bor households. Detailed algorithms to generate these three populations can be referred to the work by Mao and Bian [13]. This research further distinguished the weekday and weekend activities of individuals based on the regional travel survey. Specifically, individuals were not assigned to work during weekend (both regular and additional) except for those who work at service places. The trips to workplaces or schools (for children under 18 years old) were replaced by either staying at homes, or visiting service places/neighbor households. The simulated indi- vidual contacts during a weekday and a weekend day (Figure 1) have clear power-law distributions, consistent with the observed “scale-free” property of human social networks. The average number of contacts at the week- end is 2.4 fewer than that in the weekday, because fewer workplace contacts happen at the weekend. Flu infectivity scenarios To initialize influenza transmission through the modeled network, five infectious individuals were randomly seeded into the study area at the first day of simulation. Figure 1 Contact patterns during a weekday and a weekend. Frequency distributions of individual contacts during a regular weekday (blue) and a weekend (red). The X axis represents the number of daily contacts, and the Y axis denotes the corresponding frequency in the population. The simulation took a tri-daily time step and lasted for 200 days. During each time step, an individual held one of four infection status, i.e., susceptible, latent, infectious, or recovered. The infection proceeded for 7-10 days, with 2 days latent, 1 day asymptomatic and infectious, fol- lowed by 4-7 days infectious varying over age groups (Children: <16 years; Adults: 16-64 years; Senior: >64 years)[17]. The probability of developing symptoms once infected was set to 0.5 [18], and symptomatic individuals were referred to as ‘influenza cases’ in subsequent discus- sion. Further, a proportion of symptomatic individuals was selected to withdraw to home based on previous sur- veys of flu self-care behavior [19,20]. The stochastic simulation randomized the five infective seeds, the daily contacts of individuals and the infections through con- tacts, as well as the development of symptoms and the withdrawal-to-home of symptomatic individuals. The transmission of influenza was simulated by repeatedly tracing susceptible contacts of infectious indi- viduals, and identifying who will receive the infection in the next time step. The receipt of infection was modeled as a stochastic event determined by the age groups of receivers and the infectivity of viral strains. The viral infectivity was specified by R0 (the basic productive number), which is defined as the number of secondary cases caused by a single infected case in a wholly sus- ceptible population[21]. Relevant to this research, two scenarios of flu infectivity were established to examine the effectiveness of weekend-extension strategies: a sea- sonal flu scenario (R0 = 1.4) and a pandemic flu scenario (R0 = 2.0) [22]. Compared to the seasonal flu, the pan- demic flu occurs rarely but transmits more easily between human beings and spreads quickly all over the world, because the virus is often novel to human immune system. Under each flu scenario, a baseline out- break and the six strategies (in Table 1) were simulated and compared with one another. The baseline outbreak represented a ‘no response’ situation with no strategies applied, and served as a reference for comparison pur- poses. To validate the model, the baseline outcomes were compared to CDC weekly reports of laboratory confirmed specimens in 2004-05 flu epidemic in the study area [23]. Measures of control effectiveness The six weekend-extension strategies were assumed to be implemented when the cumulative number of