the priority setting process is to obtain best expert estimates, often relying on different individuals for different types of estimates. This could be done in a more formalized structure using group consultation techniques such as the Delphi methods or its variations for estimating unknown data.
The sensitivity analysis capabilities of SMART Vaccines provide another way to help users understand the importance of improving the quality of various types of data. If the user finds that within their context—and using their own weights—and across a wide range of possible data levels that the priority rankings do not change, then it is an indication that further investments to improve those data elements is not necessary. Alternatively, if the priority rankings are sensitive to the levels of certain data elements, then it signals the importance of investing resources to improve those data.
Currently unknowable “known unknowns.” These are data whose nature is well known but they do not exist, including the vaccines that do not yet exist, and the nature and hence the dangers of diseases that do not yet exist.
Specific data needed for SMART Vaccines can be categorized into four broad categories: population, epidemiology, economics, and vaccine-related characteristics. As mentioned in this report, the data are merely estimates derived from available information in order to offer guidance for further data collection.
The primary sources for SMART Vaccines data were publications reporting primary epidemiologic and economic data or else reporting sufficient information to derive estimates of the primary data. Studies reporting data on a national scale were given precedence over those that analyzed populations on a state, county, or provincial basis. In some instances when national estimates were unavailable, estimates from a smaller subset of a population were extrapolated to the entire population. Indirect estimates from mathematical modeling studies were consulted when firsthand data were unavailable. Specific source references are embedded in the data spreadsheets.
As part of the Phase II data collection efforts, the disease burden and vaccines data for Phase I candidates were revised to closely reflect the population measures of the 2009 U.S. and South African populations as used in the software. Data will naturally differ from year to year as updated sources become available. The examples provided here represent only a subset of all data sources and estimation approaches; they are not representative of