Aggregated Smartphone Location Data to Assist in Response to Pandemic

Abstract

The sudden, mandated closures of businesses, restaurants, and schools in response to the COVID-19 pandemic impose severe economic and social costs. People want to know when they can reopen their shops, rehire furloughed employees, and return to a life with some semblance of normalcy. Medical researchers currently estimate that the use of hospital resources will peak and ebb in most of the country in two or three weeks (mid-April to late April). Once there is slack in hospital resources, many people will be eager to return to work, worship, travel, and recreation.

Given the rapid transmission of COVID-19 and its severe health effects, stay-at-home rules and norms should be relaxed only in locations where the risk of infection is low. Data on where stay-at-home norms are practiced can help public officials identify the counties and regions where residents can go back to work and where resources (such as testing kits or public service announcements) should be allocated in order to minimize the chance of another surge of infection.

On March 26, the president announced pending guidelines in a letter to governors, including a nationwide effort to “[categorize] counties as high-risk, medium-risk, or low-risk” for COVID-19 spread. In this policy brief, we explain below how aggregated, anonymized smartphone data can serve as a powerful analytical tool for policymakers in their attempts to control COVID-19 infection while freeing households in low-risk regions and neighborhoods from stay-at-home mandates and business restrictions.

Keywords: telecommunications, regulation, cell phone, smart phone, tracking, healthcare, coronavirus, coronavirus pandemic, COVID-19, public health, economics, quarantine, economy, economic crisis

JEL Classification: O38, L63, L96, I13, I00