Optimal vaccine trial design when estimating vaccine efficacy for susceptibility and infectiousness from multiple populations.
Vaccination can have important indirect effects on the spread of an infectious agent by reducing the level of infectiousness of vaccinees who become infected. To estimate the effect of vaccination on infectiousness, one typically requires data on the contacts between susceptible and infected vaccinated and unvaccinated people. As an alternative, we propose a trial design that involves multiple independent and interchangeable populations. By varying the fraction of susceptible people vaccinated across populations, we obtain an estimate of the reduction infectiousness that depends only on incidence data from the vaccine and control groups of the multiple populations. One can also obtain from these data an estimate of the reduction of susceptibility to infection. We propose a vaccination strategy that is a trade-off between optimal estimation of vaccine efficacy for susceptibility and of vaccine efficacy for infectiousness. We show that the optimal choice depends on the anticipated efficacy of the vaccine as well as the basic reproduction number of the underlying infectious disease process. Smaller vaccination fractions appear desirable when vaccine efficacy is likely high and the basic reproduction number is not large. This strategy avoids the potential for too few infections to occur to estimate vaccine efficacy parameters reliably.
Longini
I M
IM
Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Sagatelian
K
K
Rida
W N
WN
Halloran
M E
ME
D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003140:Communicable Disease Control / Q000706:statistics & numerical data*; D004198:Disease Susceptibility / Q000453:epidemiology / Q000517:prevention & control; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D012306:Risk; D016896:Treatment Outcome; D014611:Vaccination / Q000706:statistics & numerical data*