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IDEAL’s own Ozan Candogan (University of Chicago) was recently featured in a New York Times article for his research on methods of controlling the COVID-19 pandemic. He and his co-authors, John Birge (University of Chicago) and Yiding Feng (Northwestern University) utilized cell phone data and COVID-19 infection rates to propose a closure plan that minimizes both disease spread and economic impact. This work is an impressive example of both inter-institution collaboration and application of research, both of which are tenets of our institute.
In response to the COVID-19 pandemic, many cities have instituted uniform (city-wide) suspension of economic activity to varying degrees. However, the spread of the disease relies on human-to-human contact and has an inherent spatial nature, in which infected individuals potentially infect others in locations/neighborhoods they have visited. Birge, Candogan, Feng propose a spatial epidemic spread model, which explicitly accounts for the spillovers of infections across different neighborhoods in a city. In their model, the individuals who reside in a neighborhood may spend some of their time in another neighborhood. Susceptible individuals from a neighborhood “mix” with other individuals in any of the neighborhoods in which they spend time, and they can get infected there.
The authors study the decision problem of a social planner who can restrict the economic activity in different neighborhoods. The reduction in the permitted level of economic activity in a neighborhood (i) triggers an economic loss, and (ii) decreases the number of individuals who visit that neighborhood. The latter effect reduces the infections among individuals who reside in that neighborhood as well as those who reside elsewhere but spend time there. They provide a framework for controlling the spread of the epidemic in two regimes, accounting for scenarios where the number of infections is large and small. In the first regime, their approach yields targeted closure policies that reduce infections in all neighborhoods while inducing a minimal economic loss. In the second one, their policies ensure that a small number of initial infections will not trigger a large scale contagion, again while ensuring that the economic losses are minimized.
The authors then illustrate their approach with an application to New York City (NYC). They use mobile phone data to model population movements and COVID-19 infections numbers to capture the state of the disease. Their results indicate that appropriate targeting achieves a reduction in infections with up to 12%–27% lower economic cost (by enabling 4.12 – 5.75 times more economic activity) than uniform (citywide) closure policies. The optimal policy allows for economic activity in Midtown (due to its economic importance) while imposing closures in many neighborhoods of the city (to curb the spread of the disease). Contrary to what might be intuitively expected, neighborhoods with larger levels of infections should not necessarily be the ones targeted with the most stringent economic closure measures. In addition, they show that coordination among neighboring counties and states is extremely important. For instance, depending on the policy followed by neighboring counties it may become infeasible for NYC to prevent a contagion.
Read the full The New York Times article here or the working paper here.
Research activities of the Special Quarter on Inference and Data Science on Networks kickoff on Tuesday, May 5th, with an 11-3pm virtual workshop. The Organizers of the special quarter will give short talks on topics for study during the quarter (details). Researchers interested in attending can register to join by Zoom, or livestream on Panopto (details). Members of the institute are invited to participate in an open problems session and are encouraged to join in the research activities of the special quarter.
IDEAL Spring 2020 Special Quarter on Inference and Data Science on Networks goes remote. Three remote PhD courses kickoff on Monday. PhD students from other institutions can apply to join remotely as virtual predoctoral fellows (application details on the special quarter page). In mid-April, the organizers will be holding a virtual kickoff workshop to overview key research directions for the special quarter. Research (Ph.D. students, postdocs, faculty, and research scientists) interested in joining the research efforts of the quarter should attend the kickoff workshop for details. The previously scheduled workshops are being rescheduled as remote workshops. Details of these workshops will be available shortly on the special quarter page and the IDEAL calendar.
The institute invites applications for two postdoctoral fellowships starting Fall of 2020, to conduct inter-disciplinary research that focuses on the theoretical foundations of data science. One fellowship is based at the Toyota Technology Institute at Chicago (TTIC) and one fellowship is based at Northwestern University.
Candidates will be expected to spend at least one day a week at the other campus. Ideal candidates will have interests in several of the special quarters that will be run from Fall 2020 to Spring 2022. By default, applicants will be considered for both postdoctoral positions. If you have a strong preference for one of the participating institutions, please specify in your cover letter. We encourage candidates to send applications as soon as possible. Appointments begin Fall 2020 quarter. Applications received by February 22nd, 2020 will be given full consideration.
Information for postdoctoral fellowship applicants and other ways to participate in the institute are on the participation page.
Joining forces with leading Chicago-area research institutions, Northwestern Engineering and the Weinberg College of Arts and Sciences Department of Economics colaunched the Institute for Data, Econometrics, Algorithms, and Learning (IDEAL).
IDEAL is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) institute focused on understanding key aspects of data science theory. Supported by the National Science Foundation HDR TRIPODS program, IDEAL aims to develop the foundations of data science by combining perspectives from algorithms, econometrics, and machine learning.
IDEAL in the News
Read recent media coverage of IDEAL.
June 6, 2020 | The New York Times
Coronavirus Shutdowns: Economists Look for Better Answers
November 15, 2019 | McCormick School of Engineering News
New Collaborative Institute Aims to Explore Theoretical Foundations of Data Science