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UID:20250711T223724EDT-0990k9UF2s@132.216.98.100
DTSTAMP:20250712T023724Z
DESCRIPTION:Title: Causal inference for infectious disease interventions: c
 ontagion\, confounding\, mediation\, and interference.\n\nAbstract: Forres
 t W. Crawford is an Associate Professor of Biostatistics\, Statistics & Da
 ta Science\, Operations\, and Ecology & Evolutionary Biology at Yale Unive
 rsity. He is affiliated with the Center for Interdisciplinary Research on 
 AIDS\, the Institute for Network Science\, the Computational Biology and B
 ioinformatics Program\, and the Public Health Modeling Concentration. His 
 research focuses on mathematical and statistical problems related to discr
 ete structures and stochastic processes in epidemiology\, public health\, 
 biomedicine\, and social science. He received the NIH Director's New Innov
 ator Award in 2016.\n	\n	Websites: http://www.crawfordlab.io/ - https://ysph
 .yale.edu/profile/forrest_crawford/\n\n\nRandomized and observational stud
 ies of infectious disease interventions often focus on groups of connected
  or potentially interacting individuals. When the pathogen of interest is 
 transmissible between study subjects\, interference may occur: individual 
 infection outcomes may depend on treatments received by others. Contagion 
 may induce causal dependence in outcomes\, even in the absence of treatmen
 t. Epidemiologists have introduced several competing – and incompatible – 
 formalisms for dealing with these problems. In this presentation\, I will 
 introduce a causal framework for understanding infectious disease transmis
 sion and the effects of interventions on infection outcomes. I will discus
 s a synthesis of two broad research efforts: causal inference for individu
 al vaccine effects in observational and randomized trials\, and population
 -level transmission modeling of the kind popularized during the COVID-19 p
 andemic. I outline the causal structure of contagion\, identification of m
 eaningful individual effects\, and generalization of these effects to coun
 terfactual population-level epidemic trajectories. Finally\, I describe so
 me of the pitfalls of ignoring contagion in studies of infectious disease 
 interventions.\n	\n	This is joint work with many other researchers\, includi
 ng Xiaoxuan Cai\, Olga Morozova\, Daniel Eck\, Wen Wei Loh\, and Eben Kena
 h.\n\nWeb site : /epi-biostat-occh/seminars-events/se
 minars/biostatistics\n\nPlease visit our website for the Zoom Link: https:
 //www.mcgill.ca/epi-biostat-occh/seminars-events/seminars/biostati...\n\n 
 \n\n \n
DTSTART:20221026T193000Z
DTEND:20221026T203000Z
SUMMARY:Forrest W. Crawford (Yale University)
URL:/mathstat/channels/event/forrest-w-crawford-yale-u
 niversity-342835
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