susceptibility to infection or primary prevention (AVES) is assumed to be 0.3, efficacy for secondary prevention (AVED, against disease progression) is assumed to be 0.6, and efficacy in therapy for symptomatic disease is = 1 – (1‐ AVES)(1‐ AVED), equal to 0.72. Oseltamivir efficacy for infectiousness (AVEI) is assumed to be 0.62. The values for antiviral efficacy are derived from prior studies of household infection with seasonal influenza<sup>45</sup> .

_Control objective_ : Containment of cases (i.e., symptomatic infection) at ≤ 1 per 1000.

**Table 6:** **_Effectiveness of_** **_<u>policies for control in a population of 500,000</u>_** _<u>(Longini et al.)</u>_

|_P_<br>_tion of policy_<br>_ercentage applica_|_s_<br>_Case p_|_er 1000_|_Containment_|_prop_<br>_#_<br>_ortion_|
|---|---|---|---|---|
||_R0_= 1.4|_R0_<br>7<br>= 1.|_R_<br>_0_<sup>= 1.4</sup>|_R_<br>_0_<sup>= 1.7</sup>|
|**_a_**<br>tion<br>.Nointerven|211|384|‐|‐|
|**_b_**. 80% TAP|0.13|149|98%|33%|
|**_c_**.90% GTAP|0.28|54|95%|59%|
|**_d_**.80%TAP +50% pre‐vaccination|0.02|0.16|1<br>00%|98%|
|**_e_**<br>tion<br>.80%TAP +70% pre‐vaccina|0.01|0.04|100%|100%|
|**_f_**. 70% quarantine|0.17|1|98%|57%|
|**_g_**.80%TAP +70% quarantine|0.06|0.14|100%|100%|
|**_h_**.80%TAP +70% quarantine+50% pre‐vaccination|0.02|0.03|100%|100%|

# Proportion of simulations with the policy in which cases per 1000 are ≤ 1

_Discussion_ : The results in Table 6 show a dependence on the value of _R0_ , especially for the effectiveness of TAP, GTAP, and quarantine. As _R0_ increases from 1.4 to 1.7, the required courses of antiviral in TAP (not shown) increases 78 times. Mixed strategies are more robust ( **_d_** ‐ **_e_ ,** **_g_** ‐ **_h_** ). The authors expect initial _R0_ for an emergent influenza strain to be below 2, which implies a role for antivirals at their assumed efficacy (the results are moderately sensitive to the assumed AVES value, as may be expected). This finding has led to recognition in the policy sphere of ‘threshold _R0_ ’ effects for policy effectiveness, i.e., with a more infectious viral strain, containment may be very difficult. As _R0_ may increase during transmission as the viral strain gains fitness through mutation, the authors suggest early intervention is very important. This insight is valuable.

A caveat is placed on the results. The dependence on _R0_ is in part a model artifact. Longini _et al._ calibrate the baseline model at _R0_ =1.4, which fits the historically observed overall 33% attack rate, and further to an age‐specific attack rate pattern as in Fig. 3. They then generate the other _R0_ levels (e.g., _R0_ =1.7) by increasing the transmission probability _ρ_ per contact, fixed across all age groups and contexts. Recall the equation discussed above for the effective contact rate, β = γ*ρ (where γ is the total number of contacts, and ρ is the transmission probability per contact). In the Longini _et al._ model, higher _R0_ mechanically generates more cases from the channel of ρ. Hence, single effect policies which only impact ρ fare poorly from increases in _R0_ , but mixed policies (ρ and γ) are robust.

In a real epidemic, there would be differences, but deviation from the results in Table 6 is uncertain. Targeted antiviral use may raise awareness and lower willingness to mingle, hence affecting γ, besides the effects on ρ<sup>46</sup> . Additionally, public communication, low cost yet not modeled, could cut _R_ levels by encouraging personal protective behavior.

2. **Ferguson** **_et al._ (2005)**<sup>5</sup> : Thailand plus 100‐km wide zone of contiguous border nations 18