Date Published: June 19, 2007
Publisher: Public Library of Science
Author(s): Christophe Fraser
Abstract: Fraser discusses a new study that uses exploratory modeling to tackle the difficult issue of what to do with limited stockpiles of pre-prepared influenza pandemic vaccines.
Partial Text: The global spread of highly pathogenic H5N1 influenza through poultry flocks is driving a large research effort aimed at mitigating the worst effects of an eventual human pandemic. Mathematical models and computer simulations are being used to explore different policy options in influenza pandemic control (for examples, see [1–4]). In a new study published in PLoS Medicine, Riley, Wu, and Leung  use such exploratory modelling to tackle the thorny issue of what to do with limited stockpiles of pre-prepared influenza pandemic vaccines .
Several vaccines matched to circulating avian H5N1 strains are in development [6–8], one has already been licensed by the US Food and Drug Administration [8,9], and several more are in the pipeline. Vaccine stockpiles are being planned and amassed. The issue of what to do with these stockpiles has been the subject of intense discussion. Decisions need largely to be taken in advance because of the time needed for the vaccine to be distributed and to take effect after inoculation. Some governments (e.g., the US) have drafted specific plans to prioritize vaccination of those individuals who are crucial to controlling a pandemic (such as front-line medical staff or vaccine producers) or those who are at heightened risk of influenza-related complications (such as pregnant women or the elderly) .
The international effort to control or mitigate an eventual pandemic has been substantially boosted since 2004 by the realisation that the three influenza pandemics of the twentieth century (1918, 1957, and 1968) were characterised by relatively low transmissibility, as measured by the basic reproduction number, R0 [1,2,11–19]. This number is an estimate of how many people a typical case infects over his or her infectious period, and has been estimated to lie between 1.5 and 4 for all three pandemics. There are subtleties in the estimation of R0, such as a reliance on good estimates of the infection generation time distribution , and on aggregated city-wide data (except for , in which R0 is estimated in a military camp outbreak).
Riley and colleagues’ results are quite dependent on the assumed nature of immunity, and they consider a number of different possible immunological assumptions. In the first case they assume that for any given dose, a proportion of vaccine recipients are fully protected and the rest not at all (an all-or-nothing response). In this case it is possible to explain the study results quite simply. The number protected is made greater by vaccinating more people with a lower dose. This conclusion is, of course, heavily dependent on the relation between antibody titre and immune response; for example, if there was a threshold below which a dose gave no protection at all, there would be no sense in vaccinating below that titre.
Mathematical models and computer simulations are not crystal balls, but rather tools that allow questions to be precisely defined, assumptions to be made clear, and logical deductions to be performed. So construed, they can contribute a great deal to the policy debate.