Abstract
Over the last century, effective vaccines have been developed for prevention of disease caused by many pathogens. However, effective vaccines have not yet been developed to prevent infection with the human immunodeficiency virus (HIV). A challenge in developing a vaccine to prevent HIV infection is the substantial heterogeneity in the genetic characteristics of the virus. Preventive HIV vaccines are typically constructed using only several antigens and may protect well against infection caused by virus strains similar to antigens in the vaccine, but fail to protect against disease caused by antigenically dissimilar strains. Therefore, when evaluating preventive HIV vaccines, it is important to study whether and how the efficacy of the vaccine varies with the virus’ characteristics—this field of study is called sieve analysis (Gilbert et al. 1998, 2001). The vaccine can be thought of as a sieve, inducing a strain-specific immunity that presents a barrier to infection, while there also may be “holes in the sieve,” that is, HIV strains that break through the vaccine barrier.
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Benkeser, D., Carone, M., Gilbert, P. (2018). Targeted Estimation of Cumulative Vaccine Sieve Effects. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_11
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DOI: https://doi.org/10.1007/978-3-319-65304-4_11
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