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  January 1428, 3 pm CT A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses, with Applications to Modeling and Managing the Spread of COVID-19 Claire DonnatSubhonmesh Bose, Assistant  Assistant Professor , Department of Statistics, University of Chicagoof Electrical and Computer Engineering, University of Illinois at Urbana-Champaign REGISTER FOR ZOOM WEBINAR 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. Testing and lock-down provide two important control levers to combat the spread of an infectious disease. Testing is a targeted instrument that permits the isolation of infectious individuals. Lock-down, on the other hand, is blunt and restricts the mobility of all people. In the first part of the talk, I will present a compartmental epidemic model that accounts for asymptomatic disease transmission, the impact of lock-down and different kinds of testing, motivated by the nature of the ongoing COVID-19 outbreak. In the large population regime, static mobility levels and testing requirements are characteristics that can mitigate the disease spread asymptotically. Then I present interesting properties of an optimal dynamic lock-down and testing strategy that minimizes a detailed cost of the epidemic. In the second part of the talk, I adapt the model for small populations, such as that of an educational institution, and use data from the UIUC SHIELD program’s rapid saliva-based testing strategy to estimate model parameters. Reopening strategies for educational institutions are evaluated via agent-based simulations using said parameter estimates. This talk is based on joint work with U. Mukherjee, S. Seshadri, S. Souyris, A. Ivanov, Y. Xu, and R. Watkins. Subhonmesh Bose Claire Donnat is an Assistant Professor in the Department of Statistics Electrical and Computer Engineering 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 HolmesIllinois at Urbana-Champaign. His research is in the area of power and energy systems and is geared towards enabling the integration of renewable and distributed energy resources in the modern power grid. He is interested in developing rigorous analytical frameworks, fast algorithmic architectures, and efficient market designs to help enable that integration.

  
  

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