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  December 10January 14, 3 pm CT Housing Precarity, Eviction and Inequality in the Wake of A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses, with Applications to COVID-19 Karen ChappleClaire Donnat, Assistant Professor and Chair, City and Regional Planning, UC Berkeley Tim Thomas, Research Director, Urban Displacement Project, UC Berkeley Peter Hepburn, Assistant Professor of Sociology, Rutgers University-Newark, Department of Statistics, University of Chicago REGISTER FOR ZOOM WEBINAR COVID-19 has the potential to exacerbate a severe housing and economic crisis in the U.S., which will in turn undercut public health responses to the pandemic. Ensuring housing security is vital to mitigating the spread of the virus and sustaining health, economic security, and family stability. This joint, interdisciplinary project between UC Berkeley and Princeton University brings together a group of academics and data scientists to track, analyze, and respond to pandemic-driven spikes in eviction and displacement risks. Doing so requires two central elements, both of which rely heavily on data science tools and methodologies. First, we have developed the Eviction Tracking System, an innovative tool for tracking real-time eviction filings in more than a dozen cities across the U.S. Second, we have developed a housing precarity risk model using machine learning that allows us to better analyze and predict areas at disproportionate risk of eviction, displacement, unemployment, and infection in the wake of the COVID-19 pandemic. This project provides major new sources of data that serve to inform research and public policy regarding housing and inequality in America. Karen Chapple, Ph.D., is a city planner by training who studies inequalities in the planning, development, and governance of regions in the U.S. and Latin America, with a focus on economic development and housing. Her most recent book is Transit-Oriented Displacement or Community Dividends? Understanding the Effects of Smarter Growth on Communities. Tim Thomas is an urban sociologist, demographer, and data scientist. His research examines how neighborhood change impacts racial and gender disparities in housing, segregation, and forced mobility. His work has been published in academic journals and used as evidence to inform civil and state housing law. The increasingly widespread use of affordable, yet often less reliable medical data and diagnostic tools poses a new challenge for the field of Computer-aided Diagnosis: How can we combine multiple sources of information with varying levels of precision and uncertainty to provide an informative diagnosis estimate with confidence bounds? Motivated by a concrete application in lateral flow antibody testing, we devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous and potentially unreliable data types. Our Bayesian formalism is essential in (a) flexibly combining these heterogeneous data sources and their corresponding levels of uncertainty, (b) quantifying the degree of confidence associated with a given diagnostic, and (c) dealing with the missing values that typically plague medical data. We quantify the potential of this approach on simulated data, and showcase its practicality by deploying it on a real COVID-19 immunity study. Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago. Her work focuses on high-dimensional and Bayesian statistics and their applications to biomedical data. Prior to the University of Chicago, Donnat completed her PhD in Statistics at Stanford University, where she was advised by Professor Susan HolmesPeter Hepburn is a sociologist and demographer. His research examines how changes to three core social institutions — work, criminal justice, and housing — serve to produce and perpetuate inequality. His work has been published in Social Forces, Demography, Social Problems, and the Journal of Marriage and Family.

  
  

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