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Activity Number:
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537
- SPEED: Infectious Disease, Environmental Epidemiology, and Diet
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 11:15 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #332677
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Title:
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Causal Inference for Infectious Disease Interventions in Networks
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Author(s):
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Xiaoxuan Cai* and Forrest W Crawford
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Companies:
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Yale University and Yale School of Public Health
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Keywords:
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Causal Inference;
Infectious Disease;
Survival Analysis
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
