- 49 The international quality and currency of outbreak information has improved, based on changes since SARS. The WHO official and rumor surveillance networks, along with the resources of the CDC could ensure that the delays in receiving information may be minor.

- 50 Oldham, J. 2005 “LAX plans for bird flu quarantines”, The LA Times, October 18

- 51 CDC 2003 “Revised U.S. surveillance case definition for Severe Acute Respiratory Syndrome (SARS) and update on SARS Cases ‐ United States an World dwide, December 2003”, _MMWR_ **52** (49)

- 52 Dept. of Health and Human Services 2005 _HHS Pandemic Influenza Plan_ Washington DC: DHHS

- 53 The simplest model in Riley _et al._ (2007) is: _a_ =(1 ‐ _pv*c_ )(1 ‐ _e_ ‐ _R_ 0* _a_ ) where _a_ is the attack rate, _c_ is the proportion of the population vaccinated, and _pv_ is the proportion of _c_ who receive complete protection. The first part of the equation describes the initial proportion susceptible and the latter is the overall probability of infection. If _pv_ is 39% with two 10μg doses, then if 20 out of 300 million people in the US are vaccinated, the initially susceptible proportion = 1 – (20/300)*0.39, and the attack rate drops from the no‐vaccination baseline of 73% (at _R_ 0=1.8) to 69.5%.

- 54 Fraser, C. 2007 “Influenza pandemic vaccines: spread them thin?” _PLoS Med_ **4** (6):e228

- 55 Bell _et al._ (WHO Writing Group) 2006b “Nonpharmaceutical interventions for pandemic flu, national and community measures” _Em Inf Dis_ **12** (1):88‐94

- 56 Hatchett _et al._ 2007 “Public ealth interventions and eh pidemic intensity during the 1918 influenza pandemic” _PNAS_ **104** (18):7582‐7587

- 57 In Hatchett _et al._ , ‘timing’ of policies could explain only 50% of the variation in total excess mortality across their sample of cities, an indicator closely tied to the CFR. Unknown factors that could account for variation in CFR may include: differences in general public health (which may also tie with observed differences in mortality in Afkhami 2003), varying levels of bacteria in the environment, etc. The authors rule out antigenic variation in the viruses across waves as a factor.

- 58 Carrat _et al._ 2006 “A ‘small‐world‐like’ model for comparing interventions aimed at preventing and controlling influenza pandemics” _BMC Med_ **4** (26)

- 59 See Wu _et al._ for a more involved graphical depiction of a stochastic SEIR model.

- 60 Wu _et al._ reviewed the available studies on _θ_ and found the range to be 0‐50% (based on Rvachev _et al_ ). Their assumed value is then slightly above the median.

- 61 Brownstein _et al._ 2006 “Empirical evidence for the effect of airline travel on inter‐regional influenza spread in the United States” _PLoS Med_ **3** :e401; Viboud _et al._ 2006 “Air travel and the spread of influenza: important caveats” _PLoS Med_ **3** (11):e503

- 62 Cooper _et al._ 2006 “Delaying the international spread of pandemic influenza” _PLoS Med_ **3** (6): e212 63 Epstein _et al._ 2007 “Controlling pandemic lu: the value of international air travel restrictions” f _PLoS One_ **2** (5):e401

- 64 Epstein _et al._ conclude that pandemics that originate in January in Hong Kong could, due to effective international air travel restrictions, lead to a peak in the temperate Northern hemisphere in September. Therefore, the delay of epidemic introduction would push major transmission into the influenza high season, leading to more cases. This assumes that pandemic influenza A would demonstrate similar seasonality in their infectivity as interpandemic strains. This effect would be reversed for a July start to the pandemic in the tropics, which would cause a peak in spring.

- 65 Jani, M. 2005 “Modeling the large scale disruptions of an airline network” _J Trans Engg_ **131** (4): 249‐260. For more on the hub‐and‐spoke nature of the international network in the context of communicable disease spread, see Colizza _et al._ 2006 “The role of the airline transportation network in the prediction and predictability of global epidemics” _PNAS_ **103** (7):2015‐2020

- 66 Pandemics of severity category 1‐5, based on their CFR. See CDC 2007, Table A, pp. 12 67 Susarla, A. 1996 “Plague in Surat, India” Unpublished. Clark University Gr. School of Geography. 68 Hai _et al_ . 2004 “The short‐term impact of SARS on the Chinese economy” _Asian Econ Papers_ **3** (1):57‐61

- 69 Lee _et al._ 2005 “The impact of the SARS epidemic in Taiwan: implications for human resources, labour markets, and unemployment in the service sector”, Working Paper Series, Judge Business School, University of Cambridge

- 70 Siu _et al._ 2004 “Economic impact of SARS: the case of Hong Kong” _Asian Econ Papers_ **3** (1):62‐83

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