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• Colloquium on Digital Transformation Science

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  January 28February 4, 3 pm CT Modeling and Managing the Spread Triaging of COVID-19 Patients from Audio-Visual Cues Narendra Ahuja, Research Subhonmesh Bose, Assistant Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign REGISTER FOR ZOOM WEBINAR 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. The COVID-19 pandemic has placed unprecedented stress on hospital capacity. Increased emergency department (ED) patient volumes and admission rates have led to a scarcity in beds. Bed-sparing protocols that identify COVID-19 patients stable for discharge from the ED or early hospital discharge have proven elusive given this population’s propensity to rapidly deteriorate up to one week after illness onset. Consequently, a significant number of stable patients are unnecessarily admitted to the hospital while some discharged patients decompensate at home and subsequently require emergency transport to the ED. In order to conserve hospital beds, there is an urgent need for improved methods for assessing clinical stability of COVID-19 patients. In this talk, we will describe our project’s immediate goal to develop audiovisual tools to reproduce common physical exam findings. These will be subsequently used to predict clinical decompensation from patient videos captured using consumer grade smartphones. These tools will be tested on COVID-19 and other pulmonary patient populations. We will start collecting patient data at UIC and UC hospitals in January 2021 and are developing explainable artificial intelligence and machine learning algorithms for predicting impending deterioration from health-relevant audiovisual features and provide explanations in terms of the clinical details within the electronic health record. Once validated on our patient data, the tools will provide clinical assessments of COVID-19 patients both at the bedside and across telemedicine platforms during virtual follow-ups. The techniques and algorithms developed in this project are likely to be applicable to other high-risk patient populations and emerging platforms, such as telemedicine. Narendra Ahuja is a Research Professor Subhonmesh Bose is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois 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 integrationArtificial Intelligence fields of computer vision, pattern recognition, machine learning, and image processing and their applications, including problems in developing societies. He has co-authored more than 400 papers in journals and conferences and supervised the research of more than 50 PhD, 15 MS, 100 undergraduates, and 10 Postdoctoral Scholars. He received his Ph.D. from the University of Maryland, College Park, in 1979. He is a fellow of the Institute of Electrical and Electronics Engineers, the American Association for Artificial Intelligence, the International Association for Pattern Recognition, the Association for Computing Machinery, the American Association for the Advancement of Science, and the International Society for Optical Engineering.

  
  

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