occur soon (5-year delay in base-case). The ﬁrst assump- tion implies that the consequences of a pandemic would be large in terms of the number of deaths and the healthcare resources required, whereas the second assumption implies that the costs associated with maintaining a stockpile were limited and that the future beneﬁts would not be dramati- cally reduced by discounting. Under these assumptions, even moderately effective interventions from a clinical per- spective (e.g., a vaccine with 20% efﬁcacy) may be cost- effective. When we assumed instead that the pandemic was relatively mild (case-fatality and case-hospitalization rates 5× less than base-case) and occurred 30 years later, pan- demic mitigation strategies were borderline cost-effective at best. This mild scenario still assumes a disease incidence several times that of seasonal inﬂuenza. We found that vaccination and antiviral strategies dif- fered in their sensitivity to certain key parameters (Figure 2). Because the value for value for money offered by the intervention strategies was relatively similar, even minor changes in some parameters led strategies to become domi- nated by a more effective alternative. For example, when VE increased above 41% (base-case 40%), antiviral drug treatment alone was dominated (extended) by prepandemic vaccination (strategy 3). This sensitivity highlights the in- adequacy of a base-case analysis and the need for probabi- listic sensitivity analysis (Figure 3). This analysis was restricted by a lack of accurate infor- mation on prepandemic VE. However, because any emer- gent pandemic strain is unknown, some level of uncertainty around VE is unavoidable. We assumed that a prepandemic vaccine would offer moderate protection (below that of a matched seasonal vaccine), and using probabilistic sensi- tivity analysis (Figure 3, panels B and D), we explored the risk that the emergent pandemic strain would be of a dif- ferent subtype than that chosen for the stockpiled vaccine. We did not speciﬁcally explore the use of such a vaccine in subsequent future pandemics or the separate stockpiling of adjuvant and antigen. The effectiveness of antiviral drugs can also not be known with certainty in advance. We as- sumed a static 10% resistance to antiviral drugs and varied this widely in sensitivity analysis. A more realistic model would take into consideration possible development of re- sistance over time (37), but a detailed analysis of antiviral resistance was beyond the scope of our analysis. Our model approach was deterministic so that although stochastic variation in parameters was considered, identical parameter choices led to identical model outputs. Because our analysis was limited to assessing the effect on overall attack rates and the costs and beneﬁts associated with this, rather than outputs such as daily case counts, the inﬂuence of stochasticity at the simulation level should be relatively minor. Furthermore, the importation of cases from outside the country is likely to rapidly increase counts to a level at Pandemic Inﬂuenza Mitigation Strategies which deterministic behavior dominates. A major advan- tage of a simple deterministic approach is that sensitivity analyses are not constrained by computational resources, enabling detailed uncertainty analysis. We have largely ignored issues of capacity constraint. For instance, hospital bed day capacity is likely to be se- verely strained during the peak of an inﬂuenza pandemic (38). A severe inﬂuenza pandemic is also likely to have a dramatic effect on the broader economy (39), which may not be captured well even under our societal perspective. Studies estimating the macroeconomic impact of a pan- demic are beginning to emerge (40). The failure to capture the broader macroeconomic impact makes our healthcare system perspective conservative. However, the extent to which the beneﬁts are captured (or not captured) is likely to be different for each strategy. Population prepandemic vaccine and antiviral drug treatment strategies offer substantial scope to be cost-effec- tive strategies for pandemic inﬂuenza mitigation. Unlike an- tiviral treatment strategies, population prepandemic vaccina- tion offers the possibility of containment until the arrival of a matched vaccine. The stockpiling of prepandemic vaccines should be carefully considered and take into account the cur- rent level of uncertainty and budgetary limitations. This research was investigator driven and funded by a grant from GlaxoSmithKline (GSK) Australia. C.R.M. has received fund- ing for investigator-driven research from GSK and CSL Biothera- pie and has been on advisory boards for GSK, Merck, and Wyeth. A.T.N. and J.G.W. have received research funding from GSK. Dr Newall is a lecturer at the University of New South Wales, Sydney, Australia. His research interests include the eco- nomic evaluation of prevention programs and the epidemiology of infectious diseases. References 1. World Health Organization. WHO global inﬂuenza preparedness plan. Geneva: The Organization; 2005. 2. Australian Government Department of Health and Ageing. Aus- tralian health management plan for pandemic inﬂuenza. Canberra (Australian Capital Territory, Australia): The Department; 2006. 3. US Department of Health and Human Services. HHS pandemic in- ﬂuenza plan. Washington: The Department; 2005. 4. Jennings LC, Monto AS, Chan PK, Szucs TD, Nicholson KG. Stock- piling prepandemic inﬂuenza vaccines: a new cornerstone of pan- demic preparedness plans. Lancet Infect Dis. 2008;8:650–8. DOI: 10.1016/S1473-3099(08)70232-9 5. Cioﬁ degli Atti ML, Merler S, Rizzo C, Ajelli M, Massari M, Man- fredi P, et al. Mitigation measures for pandemic inﬂuenza in Italy: an individual based model considering different scenarios. PLoS ONE. 2008;3:e1790. DOI: 10.1371/journal.pone.0001790 6. Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, et al. Strategies for containing an emerging inﬂuenza pan- demic in Southeast Asia. Nature. 2005;437:209–14. DOI: 10.1038/ nature04017 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 2, February 2010 229