Cities are scrambling to reopen their economy amid the COVID-19 crisis. We all want the same thing: to send people back to work while minimizing infections and deaths. But each reopening strategy balances economic and health outcomes in different ways—and no two cities are exactly the same.
Explore the impacts of different reopening strategies with the tool below. It illustrates our research paper, which predicts the effects of granular policy decisions in specific United States cities. The underlying model is based on anonymized interactions from cell phone data. Learn more below about our data and methodology.
The tool shows how policy choices change the number of interactions between specific types of individuals. These interactions affect how many people are able to go to work, and how many people will go on to get sick. See how some policy combinations help reduce unemployment and deaths, some prioritize the health or the economy, and some help neither cause.
A policy mix is a combination of specific policy choices across a range of categories that impact how people interact and go to work.
A stylized diagram represents how policy choices connect people and alter employment.
Stylized ⊠household clusters may connect via their ○children and ■working and □not-working adults.
Color segments show contact between stack demographic and age groups: 5-18 | 18-49 | 50-59 | 60+
The selected policy scenario leads to approximately ~ deaths and ~ *.
Now, select any other dot above to see what policy scenario it represents
*Note that these are estimates from our model meant as a guide for policy-mix selection rather than predictions to be taken at face value. These predictions assume that policy-mix is enforced in June (depending on the city) and they predict cummulative job lost and deaths till August 2. Please see our paper below and this tweetstorm for more details on our methodology or checkout our code on Github.
We are a team of academics from a range of fields within economics and transportation science who have come together to contribute rigorous, data-driven evidence to the public debate at this important time. The intent of our work is to complement the many ongoing studies across epidemiology, public health and social science by combining novel data and methodologies to forecast the public interest trade-offs between different approaches to reopening policies.
Our model is built on observed interactions from anonymized cell phone data, each characterized by age groups and occupations. In addition, our model incorporates electronic medical records data, surveys on people's ability to work from home, and insights from the growing global body of COVID-19 epidemiology literature.
While we hope that the interactive feature on this website provides an accessible and engaging way to explore the different trade-offs implied by our model, we strongly encourage interested readers to read the full paper detailing our work. There, we provide a detailed description of each data source that we use, as well as a comprehensive explanation of our methodology. In addition, we zoom in on a number of commonly proposed policies and discuss why certain policies may be more effective in Chicago, for instance, than in Sacramento.
If you are interested in applying our research to real-world applications, please contact us so that we can advise how to tailor our model to your specific needs. More details about our methodology and results can be found across the following resources: