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Abstract:
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Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogens (e.g., different strains of HIV or malaria) for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject, e.g. a qualitative readout such as the serotype, or the consensus at each amino acid location of multiple infecting pathogens.The similarity between this single entity and the vaccine inserts is quantified e.g. exact match or number of mismatched amino acids. With new technology we can now obtain the actual count of genetically distinct pathogens (and their clones) that infect an individual. This paper introduces new methods for sieve analysis that exploit this count information for studies with both passive and active surveillance of infections. The mean count of each infecting pathogen type is assumed to be log-linear and this is coupled with a proportional intensity model for time to infectious exposure
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