targets groups with high attack rates (children) and high mortality rates (infants and adults); and a contact‐based variant that removes a fraction of the most connected individuals in the modeled social network. The authors use epidemic contact network analysis to model the spread of pandemic influenza, varying the transmissibility of the viral strain by varying ρ (the transmission probability per contact). The model is based on demographic and social network data from Vancouver (Canada). Approximate results based on the graphical depiction in their paper are shown in Table 10 for discussion.

**_Table 10: Effectiveness of different targeted vaccination policies_** _<u>(Bansal et al. 2006)</u>_

|Tar<br>licy<br>geting po|ct<br>(Fra|Pr<br>e<br>io<br><br>oportion<br> ofavailab<br>n|ffe<br>v<br>le<br>gi<br>ctively<br>ccines<br>va|acc<br>%<br>ve<br>o<br>inated<sup>*</sup>,<br> to the gr<br>n|<br>up %<br>,<br>)|Attack rate, %<br>te, %)<br>(Mortality ra|
|---|---|---|---|---|---|---|
||Infants|Children|Adults|Elderly|HCW|5<sup>§</sup><br>atρ=0.1|
|No vaccine|‐|‐|‐|‐|‐|≈62(≈0.35)|
|Mortality‐based|≈75(≈25)|‐|≈50(75)|‐|≈40|≈50(≈0.26)|
|Morbidity‐based|‐|≈50 (≈95)|≈2(≈5)|≈2|‐|≈38 (≈0.25)|
|Mixed|≈10 (≈5)|≈25 (≈45)|≈10 (50)|‐|≈20|≈40 (≈0.25)|
|Contact‐based|‐|≈40 (≈80)|≈2(≈8)|≈5 (≈10)|≈100 (≈2)|≈20 (≈0.22)|

* Product of the implemented coverage level (% of each group’s size) and the group‐specific vaccine efficacy § Transmission probability for human influenza A per contact, assumed linearly related to _R0_

The authors model the results of vaccination as full protection, and those ‘effectively vaccinated’ (see note to Table 10) are removed from the epidemic network. Therefore, the contact‐based policy, which is very effective for limiting mortality and morbidity at ρ=0.15, should be disregarded as it is very model specific. The morbidity‐based policy is effective on both counts at lower transmissibility. At higher levels of ρ not shown in Table 10, the mortality‐based or mixed vaccination policies are more effective to limit mortality.

Overall, this study reiterates the insight that effectiveness depends on the intent of vaccination policy (limiting morbidity or mortality) as well as the base infectiousness of the pandemic viral strain. Morbidity‐based policies are in essence transmission‐limiting, as children and others in school settings could be most at risk of infection as well as most infectious for reasons previously described. As a result, the policy could lead to herd immunity that unambiguously lowers eventual mortality. The child‐prioritizing vaccine policies incorporating some delay in Ferguson _et al._ (policy **_16_** in Table 8) and Germann _et al_ were effective and robust to infectivity. However, when delays in the start of the policy are introduced in the model in Bansal _et al._ , prioritizing children has higher value (in terms of limiting the attack rate) only if compensated by low to moderate levels of ρ (<0.11). Multiple reintroductions of the pandemic also favor a mortality‐based policy in Bansal _et al_ . Their results are specific to the particular urban setting (Vancouver), and prone to model and parameter uncertainty (e.g., over the value of ρ). Therefore, the choice of mortality‐ based vs. children‐based policies should be driven by context, as well as early estimates of _R0_ in a pandemic. Also, these insights on targeting do not reference feasibility issues that relate to the overall antigen availability. This is considered next.

_Choices in vaccination dose size_ : Riley _et al._ (2007)<sup>24</sup> evaluate the effectiveness of an antigen‐conserving vaccination policy that covers a larger share of the population but at a lower level of protection. They use clinical trial data on efficacy for three pre‐pandemic vaccines (including one licensed by the US government). The clinical trial data provides a range of dose sizes that produced some immunogenic response, specified from minimum to maximum antigen volume. As an example, for the US government licensed vaccine this 26