Announcements

• Colloquium on Digital Transformation Science

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  July 30August 6, 3 pm CT Networked Epidemiology Models for COVID-19 Analysis and Control Carolyn Beck, Professor of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign; Tamer Ba?ar, the Swanlund Endowed Chair and CAS Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign; and Rebecca Smith, Associate Professor, Department of Pathobiology, University of Illinois College of Veterinary Medicine REGISTER FOR ZOOM WEBINAR Optimal Targeted Lockdowns in a Multi-Group SIR Model Daron Acemoglu, Institute Professor, Department of Economics, MIT REGISTER FOR ZOOM WEBINAR This colloquium will investigate targeted lockdowns using a multi-group SIR model, in which infection, hospitalization, and fatality rates vary among groups—in particular among young, middle-aged, and old patients. The model—developed by PI Daron Acemoglu with MIT Economics Professors Victor Chernozhukov, Iván Werning, and Michael Whinston—enables a tractable quantitative analysis of optimal policy. For baseline parameter values for the COVID-19 pandemic as applied to the United States, Daron Acemoglu and his colleagues find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower risk groups. The colloquium also will investigate: the impacts of group distancing, testing, and contract tracing; the matching technology; and the expected arrival time of a vaccine for optimal policies. Overall, Acemoglu’s model indicates targeted policies combined with measures that reduce interactions among groups and increase testing and isolation of the infected can minimize both economic losses and deathsSpread of epidemics over large populations has been an important research area for several centuries, studied by epidemiologists, statisticians, mathematicians, and more recently data scientists. Over the past eight months or so, the science of epidemics has accelerated at an exponential rate due to the global threat caused by COVID-19. In addition, for quite some time, mathematical models of epidemics have been developed to help predict spread and inform policy makers as to what types of containment measures might be effective.  In this lecture, Carolyn Beck, Tamer Ba?ar, and Rebecca Smith will introduce several mathematical models within a networking (graph-theoretic) framework and discuss their work as well as others’ in: analyzing stability (or instability) of the equilibrium states (endemic and disease-free equilibria); optimally determining curing rates (through antidote control techniques); optimally modifying the network structure so as to mitigate spread; and developing algorithms to assimilate real- time testing data into networked epidemiological models. The speakers will discuss the plans of their project team—also comprised of Prashant Mehta (PI) and Matthew West of the University of Illinois at Urbana-Champaign and Philip E. Paré of Purdue University—in the development of models, algorithms, and software tools to support the state-level PCR (polymerase chain reaction) and serological testing efforts.

  
  

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Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)