Forrest W. Crawford (Yale University)
Title:ÌýCausal inference for infectious disease interventions: contagion, confounding, mediation, and interference.
´¡²ú²õ³Ù°ù²¹³¦³Ù:ÌýForrest W. Crawford is an Associate Professor of Biostatistics, Statistics & Data Science, Operations, and Ecology & Evolutionary Biology at Yale University. He is affiliated with the Center for Interdisciplinary Research on AIDS, the Institute for Network Science, the Computational Biology and Bioinformatics Program, and the Public Health Modeling Concentration. His research focuses on mathematical and statistical problems related to discrete structures and stochastic processes in epidemiology, public health, biomedicine, and social science. He received the NIH Director's New Innovator Award in 2016.
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Randomized and observational studies 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 treatment. 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 transmission and the effects of interventions on infection outcomes. I will discuss a synthesis of two broad research efforts: causal inference for individual vaccine effects in observational and randomized trials, and population-level transmission modeling of the kind popularized during the COVID-19 pandemic. I outline the causal structure of contagion, identification of meaningful individual effects, and generalization of these effects to counterfactual population-level epidemic trajectories. Finally, I describe some of the pitfalls of ignoring contagion in studies of infectious disease interventions.
This is joint work with many other researchers, including Xiaoxuan Cai, Olga Morozova, Daniel Eck, Wen Wei Loh, and Eben Kenah.
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