The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P < 0.001). We found that an increase in a county’s average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1–1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

Purpose: The United States has the highest number of confirmed COVID-19 cases in the world to date, with over 94,000 COVID-19-related deaths. The true risk of a COVID-19 resurgence as states prepare to reopen businesses is unknown. This paper aims to classify businesses by their risk of transmission and provide a method to measure traffic and risk at businesses as states reopen in order to quantify the relationship between the density of potential super-spreader businesses and COVID-19 cases. Methods: We constructed a COVID-19 Business Transmission Risk Index based upon the frequency and duration of visits and square footage of businesses pre-pandemic in 2019 in 8 states (Massachusetts, Rhode Island, Connecticut, New Hampshire, Vermont, Maine, New York, and California). We used this index to classify businesses as potential super-spreaders. Then, we analyzed the association between the density of super-spreader businesses in a county and the rate of COVID-19 cases. We performed significance testing using a negative binomial regression. The main outcome of interest is the cumulative number of COVID-19 cases each week. Results: We developed an index to monitor traffic and quantify potential risk at businesses and found a positive association between the density of potential superspreader businesses and COVID-19 cases. A 1 percentage point increase in the density of super-spreader businesses is associated with 5% higher COVID-19 cases, all else equal. Conclusion: Higher densities of potential super-spreader businesses are associated with higher rates of COVID-19 cases. This may have important implications for how states reopen potential super-spreader businesses. Our main contribution is an index that provides a way for policymakers to monitor traffic and potential risk at businesses as states reopen.

A Correction to this paper has been published: https://doi.org/10.1038/s41746-021-00444-1