|
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
|
|
Abstract:
|
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.
