Mao BMC Public Health 2011, 11:522 http://www.biomedcentral.com/1471-2458/11/522 Table 2 Sensitivity of continuous weekend-extension strategies to compliance levels R0 = 1.4 Overall attack rates % (95% CIa) Relative effectivenessb R0 = 2.0 Overall attack rates % (95% CI) Relative effectiveness Effectiveness Compliance Baseline Mon Mon+Tue Mon+Tue +Wed 100% 90% 75% 50% 100% 90% 75% 50% 100% 90% 75% 50% 18.61 (18.54, 18.71) 16.14 (16.03, 16.26) 16.41 (16.28, 16.54) 16.83 (16.74, 16.96) 17.53 (17.39, 17.65) 11.82 (11.58, 12.11) 12.98 (12.80, 13.17) 14.23 (13.93, 14.46) 15.99 (15.81, 16.22) 3.26 (1.47, 4.79) 7.24 (4.87, 8.21) 10.92 (10.27, 11.37) 14.45 (14.13, 14.80) 0.00 0.13 0.12 0.10 0.06 0.36 0.30 0.23 0.14 0.82 0.61 0.41 0.22 26.43 (26.36, 26.53) 25.52 (25.43, 25.62) 25.58 (25.49, 25.67) 25.74 (25.67, 25.82) 26.01 (25.95, 26.09) 23.84 (23.73, 23.94) 24.10 (24.00, 24.22) 24.55 (24.41, 24.96) 25.28 (25.16, 25.36) 20.30 (20.16, 20.42) 21.15 (21.01, 21.34) 22.31 (22.17, 22.43) 24.00 (23.81, 24.15) Page 5 of 10 0.00 0.03 0.03 0.03 0.02 0.10 0.10 0.07 0.04 0.23 0.20 0.16 0.10 a95%CI stands for the 95% confidence intervals from 50 model realizations. bRelative effectiveness = (Baseline attack rate -Strategy-specific attack rate)/Baseline attack rate. Table 2 compares the overall attack rates and relative effectiveness resulted from three continuous weekend-extension strategies (Mon, Mon+Tue, and Mon+Tue +Wed). Sensitivity analysis was conducted on each strategy under five compliance levels of 0%, 50%, 75%, 90%, and 100%. level from 50% to 90% can improve the control effec- tiveness by 2-3 times (F = 4.40, p-value = 0.008 from ANOVA). With respect to pandemic flu, the compliance levels caused less variation in control effectiveness but remain statistically different (F = 3.36 and p-value = 0.02). Even the 100% compliance cannot help any of the three strategies to dampen the flu pandemics. The spatial effectiveness against seasonal flu was of interest as well. For the ease of description, the intensity maps divided the study area into three zones: a central business district (CBD), a transition zone, and a suburb (Figure 4). The zonal delineation was based on densities of households and businesses, as well as the layout of major roads. The ‘Mon’ strategy (Figure 4a) failed to prevent the wide dispersion of influenza. The entire study area was dominated by high intensity of infections (>200 infections per km2). The ‘Mon+Tue’ strategy (Fig- ure 4b) apparently mitigated infections in all three zones, but could not downsize the affected areas. The ‘Mon+Tue+Wed’ strategy had an outstanding effect on the spatial dispersion of influenza (Figure 4c). The affected areas were largely reduced, leaving the extensive suburb with only a small number of infection clusters. Discontinuous weekend-extension strategies Compared to the continuous strategies, the discontinu- ous strategies resulted in epidemic curves with more oscillations (Figure 5). This is because the days on and Figure 3 Continuous weekend-extension strategies (1). Simulated epidemic curves from three continuous weekend-extension strategies, in comparison with the baseline epidemic. Y axis indicates the number of newly influenza cases per day during the 200 day simulation. All curves are averages of 50 randomly seeded simulation runs.