  Results from randomized controlled trials (RCTs) help determine vaccination
strategies and related public health policies. However, defining and
identifying estimands that can guide policies in infectious disease settings is
difficult, even in an RCT. The effects of vaccination critically depend on
characteristics of the population of interest, such as the prevalence of
infection, the number of vaccinated, and social behaviors. To mitigate the
dependence on such characteristics, estimands, and study designs, that require
conditioning or intervening on exposure to the infectious agent have been
advocated. But a fundamental problem for both RCTs and observational studies is
that exposure status is often unavailable or difficult to measure, which has
made it impossible to apply existing methodology to study vaccine effects that
account for exposure status. In this work, we present new results on this type
of vaccine effects. Under plausible conditions, we show that point
identification of certain relative effects is possible even when the exposure
status is unknown. Furthermore, we derive sharp bounds on the corresponding
absolute effects. We apply these results to estimate the effects of the ChAdOx1
nCoV-19 vaccine on SARS-CoV-2 disease (COVID-19) conditional on post-vaccine
exposure to the virus, using data from a large RCT.
