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.