**Table 4:** **_Comparison of two model archetypes for predicting_** **_<u>pandemic influenza transmission</u>_**

|_Characteristic_|_ge­struc_<br>_Deterministic SEIR:a_<br>_ture_|_Stochastic –spatial model _|
|---|---|---|
|Source study|Part ofa proposed WBstudy|Longini_et al._ (2005)|
|Contact rates by age|Exogenous constants<br>#|Exogenous constants|
|Attack rates by age|Endogenously derived|Endogenously derived<sup>§</sup>|
|Contactrates by<br>c<br>lo ation|Exogenous constants<sup>#</sup>|Exogenous constants|
|Attack rates bylocation|Endogenously derived|Endogenously derived|
|Whole epidemic_R0_|Not modeled|Exogenously varied|
|Whole epidemic GAR|Endogenously derived|Endogenously derived|
|Raw data needs|Demographic data, seasonal influenza<br>contactrate estimates|Demographic data, geographic<br>data, contactrate estimates|
|Attack rates by policy scenario|Proposed to bemodeled|Endogenously derived|
|Primary policy use|Describe population‐level dyna<br>mics,<br>estimate of no.of infections|Describe individual‐level dynamics<br>he lth measures<br>test public<br>a<br>,|

> # Proposed to create separate matrices of child‐child, adult‐child, etc., _effective_ contact rates for cities by seasons.

> § Calibrated to fit patterns of age‐structured attack rates from prior pandemics at various _R0_ .

**_Mathematical or static scenario models of pandemic influenza_** : Table 4 suggests that existing pandemic flu transmission model archetypes suit different policy questions. If interest is limited to impact analysis of the base case of a pandemic, then complex deterministic or stochastic transmission models are not required. This logic drives recent non‐epidemiological impact analyses, with scenarios based on 1918<sup>8</sup> . However, some quasi‐ epidemiological detail is required if the scenarios are to be more realistic. For resource allocation decisions, the impact estimates should be as contextual as is feasible.

Two studies are briefly discussed that use a mathematical or static scenario‐based model to inform domestic policy decisions. Meltzer _et al._<sup>9</sup> use a Monte Carlo ‘mathematical simulation’ model to estimate the impact in the US of a pandemic of influenza and scenarios of related key interventions. A similar model is used by Doyle _et al._<sup>39</sup> for France. The GAR<sup>40</sup> is varied in 5 percentage point increments from 15% to 35%. The total numbers of cases at any GAR are then distributed across age <u>groups in two pattern distribution scenarios:</u>

|_Age group_|_Pat_<br>_A_<br>_tern_|_Pat_<br>_B_<br>_tern_|
|---|---|---|
||% ofallcases|% ofallcases|
|0‐19 years old|40|46|
|20‐64 yrs old|53.1|46.7|
|65+yrs old|6.8|7.3|

Based on upper and lower estimates of age‐specific attack rates from 1918, 1928‐29, and 1957 influenza epidemics in the U.S (1918 and 1957 were pandemics).

The three age groups also differ in the proportions in each with pre‐existing conditions that cause complications once an individual has influenza. These complications are health outcomes such as severe illness and death, and rates of these per age group are distributions with upper/lower limits based on statistics from past influenza epidemics in the US. The result of the study is not a single estimate of the number of clinical illnesses or a dollar figure, but a range for such variables. The authors describe their intent as “altering a number of variables and evaluating how the results affect key (policy) decisions<sup>41</sup> .” In van Genugten _et al._<sup>42</sup> , researchers using a ‘static’ scenario method to test policies for pandemic influenza in the Netherlands. The GAR was exogenously set at 30%, and an age distribution of the cases was obtained by applying a pattern based on seasonal influenza in the Netherlands. The ‘scenarios’ are separate policy interventions that reduce adverse 14