To test the robustness of the observations for diagnostic approaches D2 to D4 within each intervention policy, the simulations are repeated but with varying levels of testkit sensitivity (50 and 100% sensitive). In general, increasing the sensitivity of the testkits has the effect of decreasing the total attack rate ( figure 10) . Surprisingly in (c) Figure 8 . Effects of varying compliance rates. The graphs show the changes of the (a) total attack rate, (b) drug usage and (c) testkit usage with increasing compliance rates for a particular diagnostic approach (D2). In particular, for policies P4a and P4c (highlighted in dashed circles), higher compliance rate (from 50 to 70%) will result in lower total attack rate, but without a corresponding increase in the resources required (light grey bar, 30% compliance; striped bar, 50% compliance; checked bar, 70% compliance). . Reduced drug efficacy. The black dots are the various data points obtained by simulating the model using base drug efficacy (base data points), while the white ones are derived using reduced drug efficacy (reduced data points). The aggregate cost for the base data points differs slightly from the original plot (figure 6) due to the increased set of data points used in the computation. Most of the reduced data points are shifted by a small amount, except for those obtained using policies P4a and P4c, as indicated by dashed circles. some cases, a highly sensitive, but slow testkit, coupled with proper diagnostic approaches, can bring about a lower total attack rate as compared with simply assuming that all patients are infected with pandemic influenza (figure 10, diagnosis D4 with policies P2a, P2b, P3b and P4b). One possible explanation is that confining the pandemic-positive patients in the hospital tends to remove them from infectious circulation while they are at their peak infectiousness.