**Table 9:** **_Effectiveness of_** **_<u>policies for control in a US pop. of 281 million</u>_** _<u>(Germann et al. 2006)</u>_

||_u_<br>_Illness_<br>_mulati_<br>_es :c_|_ve incide_<br>_er 100_<br>_nce p_|
|---|---|---|
|_Policy/Mixed strategy_|_R0_=1.6|_R0_=1.9|
|**_1_**.Nointervention|32.6|43.5|
|**_2_**. TAP with unlimited stockpile<sup>*</sup>|0.06 (2.8 mn.)|4.3 (182 mn.)|
|**_3_**. Dynamic vaccination:one dose,random<sup>†</sup>|0.7|17.7|
|**_4_**. Dynamic vaccination:one dose, child‐first<sup>†</sup>|0<br>.04|2.8|
|**_5_**. Dynamic vaccination:two doses,random<sup>†</sup>|3.2|33.8|
|**_6_**<br>wo doses, child‐first<sup>†</sup><br>. Dynamic vaccination:t|0.9|25.1|
|**_7_**.Schoolclosure<sup>¶</sup>|1.0|29.3|
|**_8_**. Local social distancing<sup>¶</sup>|25.1|39.2|
|**_9_**. Travel restrictions<sup>§</sup>|32.8|44.0|
|**_10_**. Localsocialdistancing and travel restr tions<sup>¶</sup><br>ic|19.6|39.3|
|**_11_**. TAP<sup>*</sup>, schoolclosure<sup>**</sup>, & socialdistancing<sup>**</sup>|0.02(0.6mn.)|0.07 (1.6mn.)|
|**_12_**. Dynamic one dose random vaccination<sup>†</sup>, social<br>distancing<sup>¶</sup>, travel restrictions<sup>¶§</sup>, & schoolclosure<sup>**</sup>|0.04|0.2|
|**_13_**<br>ial<br>**<br>. TAP<sup>*</sup>, dynamic one dose random vaccination<sup>†</sup>, soc<br>distancing<sup>¶</sup>, travel restrictions<sup>¶§</sup>, & school closure|0.02 (0.3 mn.)|0.03 (0.7 mn.)|
|**_14_**. Dynamic one dose child‐first vaccination<sup>†</sup>, social<br>distancing<sup>¶</sup>, travel restrictions<sup>¶§</sup>, & schoolclosure<sup>**</sup>|0.02|0.2|

* For 60% cases. Policy begins 7 days after pandemic alert (brackets: antiviral supply needed, in millions) † 10 mn. doses per wk. for 25 wks., timed so individuals develop immunity on the date of first US index case. ¶ Intervention starting 7 days after pandemic alert        ** Intervention starting 14 days after pandemic alert. § Reduction in long‐distance travel to 10% of normal frequency, occurs during entire simulated epidemic

_Discussion_ : Table 9 above closely follows the results as reported in the study. Given comparable vaccine _efficacy_ to Ferguson _et al._ (2006), the _effectiveness_ of dynamic vaccination policy is very high, lowering cumulative incidence (analogous to the attack rate in this context) to 0.04% at _R0_ =1.6. The best vaccination policy ( **_4_** in Table 9) prioritizes children and is antigen saving by using only one dose. The distribution is timed such that a proportion of the population has some immunity before the first US case. This proportion depends on the number of weeks by which the vaccination program precedes a global outbreak. In comparison, Ferguson _et al._ (2006) model pre‐pandemic vaccine administered to a fixed 20% of the population while prioritizing children, which in combination with other interventions can lower attack rates to as low as 1% at _R0_ =1.7 (not shown in Table 8).

The threshold effects of the basic reproductive number _R0_ are very intense in their study for the increase from 1.6 to 1.9 (results for _R0_ >1.9 not shown in Table 9 imply single policies will fail). But even at higher levels of _R0_ , combination strategies with a dynamic vaccination policy perform very well, and also limit the need for large antiviral stockpiles.

The authors consider the potential feasibility issues for TAP carefully and find that the policy – though successful at lower levels of _R0_ – requires many onerous assumptions of policy preparedness and is vulnerable to uncertainty. School closure and other social distancing are considered, but do not generate sufficient benefits given the social cost. However, a TAP policy in combination with social distancing (strategy **_11_** ) is valuable for containing the attack rate, such that a larger proportion of the population remains eligible until the point a well‐matched pandemic vaccine becomes available (strategy **_13_** ). Such a mixed strategy reduces the overall caseload dramatically. This combination was as 24