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Activity Number: 493
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #311484 View Presentation
Title: Counting Process Models for Infectivity in Familial Disease Clusters
Author(s): Forrest W. Crawford*+ and Daniel Zelterman
Companies: Yale School of Public Health and Yale
Keywords: Counting process ; continuous-time Markov chain ; dependent bernoulli ; correlated outcomes ; familial disease clustering
Abstract:

Epidemiological outcomes in family clusters are often dependent. Usually families are subject to two kinds of risk: one due to household- or cluster-level exposure and one due to contact with other affected family members. Untangling these sources of risk to find the per-unit disease risk is a vital task in epidemiology. In this talk, I derive a family of counting distributions for sums of dependent Bernoulli variables using principles from continuous-time Markov epidemic models. I derive efficient algorithms for regression with covariates. The approach allows estimation of relative risk, while controlling for dependency of responses due to infectivity or contagion within families.


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