Unwind the quarantine with anonymous smartphone data

For example, areas with high levels of infection and hospitalization — and where location data shows higher-than-expected resident mobility — should be categorized as “high-risk.” Areas with few infections and strong social distancing are “low-risk.” “Moderate-risk” areas would fall somewhere in between, or be strong in one metric but not the other.

With a better sense of where the risk is high and where it is low, policymakers could then do three important things. First, they could reallocate masks, ventilators, medical labor, and other high-value resources to the higher-risk areas. Second, once testing and masks become more widely available, they could push these supplies to lower-risk areas. Asymptomatic carriers of the virus would be better aware of the risk they pose to others and more likely to self-isolate. Officials could slowly begin relaxing some social distancing enforcement policies. Third, they would have an opportunity to better assess and improve the efficacy of stay-at-home orders and education by comparing the outcomes in different states and cities.

Relaxing social-distancing policies will need to be done with caution, accounting for each location’s unique conditions, such as transportation hubs, common travel destinations, and proximity to other, higher-risk areas.