Artificial Intelligence

From foodborne disease to COVID-19: network and data science to model, understand, and prevent large-scale diseases

Friday, June 05, 2020, 2:00pm - 3:00pm PDTiCal
Virtual via Zoom (see link below)
This event is open to the public.
AI Seminar
Abigail L. Horn, USC

Abstract: This talk will describe various applications of projects in "systems epidemiology” to understand, model, and mitigate the spread of large-scale infectious and chronic diseases, through the use of approaches from network science, data science, and probabilistic modeling and the integration of digital trace data. I will first discuss a long-running project to improve ability to identify the source of large-scale outbreaks of foodborne disease, combining mathematical models of contamination transmission with models of the network structure of the food supply system informed by logistical data. I will then introduce a novel project to leverage high-resolution mobility data to study the patterns of food access in Los Angeles for diverse populations, and how this is related to socio-demographics, diet, and obesity. I will close by mentioning recent COVID-19 modeling work to understand and project the spread of the outbreak in Los Angeles County with a specific focus on the impact on at-risk groups, which integrates survey data on population prevalence of health factors with digital data on mobility.

Bio: Abigail Horn is a Postdoctoral Fellow in the Department of Preventive Medicine at the University of Southern California and a member of the Center for Applied Network Analysis (CANA). Her research interests involve network epidemiology, probabilistic modeling and data science in the context of public health, with a focus on foodborne diseases and diseases of diet. She received a Ph.D. from the Institute for Data, Systems and Society at the Massachusetts Institute of Technology and a Bachelor’s in Physics from the College of Creative Studies at the University of California, Santa Barbara. Prior to joining USC she led a research project at the German federal-level food protection agency to develop, implement, and evaluate algorithms and decision support systems for modeling food supply networks to identify the source of large-scale outbreaks of foodborne disease. Her work has been funded by the NIH, the Robert Wood Johnson Foundation, the Bayer Foundation, the German Research Foundation, and the Santa Fe Institute.

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