Vaccination of one person may prevent the infection of another either because (i) the vaccine prevents the first from being infected and from infecting the second or because (ii) even if the first person is infected, the vaccine may render the infection less infectious. We might refer to the first of these mechanisms as a contagion effect and the second as an infectiousness effect. In this paper, for the simple setting of a randomized vaccine trial with households of size two, we use counterfactual theory under interference to provide formal definitions of a contagion effect and an infectiousness effect. Using ideas analogous to mediation analysis, we show that the indirect effect (the effect of one individualu27s vaccine on anotheru27s outcome) can be decomposed into a contagion effect and an infectiousness effect on the risk difference, risk ratio, odds ratio and vaccine efficacy scales. We provide identification assumptions for such contagion and infectiousness effects, and describe a simple statistical techniques to estimate these effects when they are identified. We also give a sensitivity analysis techniques to assess how inferences would change under violations of the identification assumptions. The concepts and results of this paper are illustrated with sample vaccine trial data.
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