# **<u>Table 2</u>** <u>:</u> **_<u>Historical Estimates and Modeling Estimates of CFR by scenario</u>_**

|**Scenarios based on history**|**Lower**|**d u**<br>**Me**<br>**m**<br>**i**|**Upper**|**‘Ultra’**|
|---|---|---|---|---|
|Category 5 or Like1918‐20|0<br>.2‐0.5|2.5|4||
|Category2or Like1957‐58|0.04||0.27||
|Category1or Like1968‐69|0.01|0.013|0.07||
|**Other modeling estimates**|||||
|US, StdRisk0‐19 yrs|0.001|0.002|0.013||
|US, High Risk 0‐19 yrs|0.013|0.022|0.765|M<br>_a_<br>eltzer_et_<br>_l._<sup>9</sup>|
|US,McKibbin& Sidorenko*|0.023|0.233|1.166|2.333|

* Same for all age groups, and run for Mild/Moderate/Severe/Ultra.

Similar to infectivity, any projected patterns in virulence – across age groups, location, or ethnicity – are important for setting priorities in prevention and mitigation of a pandemic. These patterns are also significant towards the macroeconomic impact. It is uncertain which factor, if any, will cause the definitive pattern in a future pandemic. It is now known that mortality in a case of pandemic influenza A virus of close avian origins – as suspected for 1918‐20<sup>10</sup> – results from an inimical feedback process in the immune system, termed a ‘cytokine storm’. This response has also been observed in human cases of the current avian H5N1 virus. Here, a healthier immune system, as in younger people, may result in higher risk for severe health outcomes. Still, the age pattern of excess mortality in the 1918‐20 pandemic – higher in young adults compared to the elderly – was not repeated in 1957‐58 or 1968‐69 (these pandemic virus strains had some avian influenza genes). For other patterns, a regression study using international data from 1918‐20 found that low per‐capita income was a statistically significant explanatory factor of higher mortality<sup>11</sup> . A study of the same pandemic in Iran found significant rural‐urban differences in mortality<sup>12</sup> .

**_Effective contact_** : Given the recurrence of the idea of effective contact in the modeling of influenza transmission, a short discussion is provided. An effective or sufficient contact between an infected and an uninfected, susceptible individual is one able to transmit infection. Depending on the requirements of the transmission model, an effective contact ‘rate’ (no. of contacts per unit time) or ‘probability’ (expressing the likelihood of making at least one effective contact per unit time) can be defined. Given a base infectivity of the influenza virus, the effective contact rate/probability varies across individuals based on the volume, frequency, and proximity quotients of their contacts. For example, compared to adults, elementary school children may meet a larger number of contacts in playgrounds and classrooms. Besides the fact that children may shed more virus, the transmission risk per contact may be higher since they engage frequently and get in closer proximity (within three to six feet of each other<sup>13</sup> ). However, proximity becomes a less strict criterion in closed, cool areas with minimum airflow, such as an aircraft cabin. In another example of variation, a rural, dispersed population may have lower volume and frequency of contacts.

In modeling studies, effective contact properties are purposively fixed for certain groupings of individuals. The parameter values are often estimated from observational data using an equation such as β _i_ = γ _i_ *ρ _i_ (where β is the effective contact rate, γ is the total number of contacts per unit time, and ρ is the risk of transmission given contact, with _i_ as a subscript referring to the particular population group). The estimates of total contact rates across various groups describe a social network. Other aspects of social network analysis are also important for modeling influenza transmission, as will be discussed later.

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