  Causal identification of treatment effects for infectious disease outcomes in
interconnected populations is challenging because infection outcomes may be
transmissible to others, and treatment given to one individual may affect
others' outcomes. Contagion, or transmissibility of outcomes, complicates
standard conceptions of treatment interference in which an intervention
delivered to one individual can affect outcomes of others. Several statistical
frameworks have been proposed to measure causal treatment effects in this
setting, including structural transmission models, mediation-based partnership
models, and randomized trial designs. However, existing estimands for
infectious disease intervention effects are of limited conceptual usefulness:
Some are parameters in a structural model whose causal interpretation is
unclear, others are causal effects defined only in a restricted two-person
setting, and still others are nonparametric estimands that arise naturally in
the context of a randomized trial but may not measure any biologically
meaningful effect. In this paper, we describe a unifying formalism for defining
nonparametric structural causal estimands and an identification strategy for
learning about infectious disease intervention effects in clusters of
interacting individuals when infection times are observed. The estimands
generalize existing quantities and provide a framework for causal
identification in randomized and observational studies, including situations
where only binary infection outcomes are observed. A semiparametric class of
pairwise Cox-type transmission hazard models is used to facilitate statistical
inference in finite samples. A comprehensive simulation study compares existing
and proposed estimands under a variety of randomized and observational vaccine
trial designs.
