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Activity Number: 474 - SPEED: Infectious Disease, Environmental Epidemiology, and Diet
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #329027 Presentation
Title: Causal Inference for Infectious Disease Interventions in Networks
Author(s): Xiaoxuan Cai* and Forrest W Crawford
Companies: Yale University and Yale School of Public Health
Keywords: Causal Inference; Infectious Disease; Survival Analysis

Measuring the effect of infectious disease interventions is a major challenge in epidemiology because the outcome of interest - infection - may be transmissible between study subjects. This complication means that individuals' infection outcomes may depend on the treatments and outcomes of other individuals, a phenomenon known as "interference" or "spillover". Infectious disease interventions are unique because they can have distinct effects on individual-level susceptibility to disease, and infectiousness once infected. We propose a general stochastic model of infectious disease transmission in continuous time that significantly generalizes existing models used to define causal vaccine effects. We develop a semi-parametric framework for statistical inference of vaccine direct and indirect effects that permits regression adjustment for baseline confounders. Large-sample statistical properties are established under the theory of counting processes, and performance of the procedure is verified by simulations.

Authors who are presenting talks have a * after their name.

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