Mao BMC Public Health 2011, 11:522 http://www.biomedcentral.com/1471-2458/11/522 influenza cases exceeded 1,000 (about 1‰ of the popu- lation). The simulation results recorded the time period and location of every infection event. The results were aggregated into a daily epidemic curve, which depicted the number of new influenza cases per day during the course of an epidemic. Characteristics associated with the epidemic curve were also extracted, including an overall attack rate (the total percent of influenza cases in the population), an epidemic peak, and peak time. An outbreak was deemed to be successfully controlled if the overall attack rate can be reduced below 10% [5]. In addition, the attack rate <5% was used as a criterion for evaluating if an outbreak can be further prevented, since reported flu epidemics often have an attack rate of 5% or higher[1]. To compare strategies, a relative effective- ness was calculated as a ratio of the attack rate reduced by a strategy to the baseline attack rate. This relative effectiveness ranges from 0 to 1, with 0 indicating no effectiveness on influenza outbreaks. Further, a sensitiv- ity analysis and ANOVA test were conducted upon a range of compliance levels, including 100%, 90%, 75%, 50% and 0% of businesses, accounting for the situation that business owners were reluctant to suspend their businesses. This research also displayed the spatial effectiveness of mitigation from each strategy. The simulation results were converted from points of infections to grid cells of infections per km2, as infection intensity maps. A kernel density function was utilized to interpolate the total number of infections at every cell location during the 200-day simulation. The cell size was set to 50 × 50 m because it approximated the average extent of land par- cels in the study area. An effective strategy was expected to reduce the infection intensity at every location and confine the spatial extent of affected areas. Both the epi- demic curves and infection intensity maps showed the mean of 50 model runs to average out the variation caused by the stochastic simulation. Results Baseline outbreaks of seasonal and pandemic flu The modeled epidemic curve under R0 = 1.4 matched well with the observed time course of a seasonal flu out- break in year 2004-05 (Figure 2), although the magni- tude of simulated curve was larger than the reported. This is because a large proportion of infected people may not manifest symptoms or seek health care, and thus the number of influenza cases was highly under- reported by CDC. In this sense, the simulation model performed well in predicting the time trend, and at least allowed a representation of the worse case result. The pandemic flu (R0 = 2.0) caused an earlier and higher epidemic peak with more influenza cases, because this virus strain is far more contagious and spreads faster. Page 4 of 10 Figure 2 Baseline scenarios of seasonal and pandemic flu. Simulated baseline outbreaks of influenza epidemic (Green) and pandemic (Red), compared to the reported influenza epidemic by CDC in the same area for year 2004-5 (Blue bars). The bars represent the number of confirmed influenza specimens per week during the epidemic 2004-5(Left Y axis). The curves represent newly influenza cases per week during the course of an epidemic or a pandemic (Right Y axis). Continuous weekend-extension strategies In the case of seasonal flu, the effectiveness of three continuous strategies differ statistically from one another (F = 6.89 and p-value = 0.02 from ANOVA). Extending the weekend to Monday mildly reduced the overall attack rate from 18.61% to 16.14% (Table 2) and slightly delayed the peak time by one week (Figure 3a). Apparent mitigation effects occurred when the weekend was extended by two consecutive days or longer. Given a high compliance level (>90%), the ‘Mon+Tue’ strategy can lessen the overall attack rate close to 10% and post- pone the peak time by almost 7 weeks. Extending the weekend by three consecutive days (Mon+Tue+Wed) was capable of controlling the attack rate under 10%, given a compliance level above 75%. Provided a full compliance, the ‘Mon+Tue+Wed’ strategy can even pre- vent the epidemic by curtailing the attack rate far below 5%. Turning to the pandemic flu scenario (Figure 3b), the three continuous weekend-extension strategies did not perform as effectively as in the seasonal flu scenario, but their effectiveness remained statistically different (F = 14.7 and p-value = 0.001). Extending the weekend by one or two days produced a few effects on the overall attack rate and peak time. Only the three-day continu- ous extension can significantly lower the overall attack rate from 26% to 20% (Table 2). All three strategies were not capable of controlling the pandemic flu independently. The compliance levels of businesses had profound effects on the control effectiveness (Table 2). The longer the extension of weekend, the lower the compliance level was needed to achieve the same control effective- ness. For the seasonal flu, increasing the compliance