On Monday, the CDC published an early report on the "timing of community mitigation" strategies aimed at slowing the spread of COVID-19. The study focused on four major metropolitan areas—Seattle, San Francisco, New York City, and New Orleans—between February 26 and April 1. Here are the key findings:
- We already know that measures like social distancing, regular cleaning, and wearing fabric masks can help reduce the transmission of viral diseases during a pandemic.
- During the time period studied, movement in these cities slowed as local governments implemented health policies and as the number of reported cases surged.
- While it's possible that stay-at-home orders actually kept people home, the data doesn’t clearly show that this led to a significant slowdown in the virus’s spread.
Breaking It Down
The report found that people didn’t significantly reduce their movements when a state of emergency was declared. It took more concrete actions—like limiting mass gatherings, closing schools, and implementing other restrictions—to slow down mobility. As the White House launched its “30 Days to Slow the Spread†initiative, movement dropped further. And once stay-at-home orders were enforced across states, mobility decreased even more. In fact, while 80% of people were leaving their homes on February 26, by April 1, that number had dropped to about 50% on average across all states.
What remains unclear is whether these changes in behavior actually helped curb the spread of the virus. In all four cities, case numbers continued to rise, even as people moved less. The CDC’s graphs showed a slight downward trend in mobility, but case counts shot upward sharply. Meanwhile, the three-day average percentage change in cases fluctuated before gradually decreasing.
So how do we make sense of this? It seems that multiple public health measures were needed to get a large portion of the population to stay home. At the same time, the long incubation period of the virus meant that many cases went undetected for days or weeks, leading to sudden spikes once symptoms appeared and testing increased.
Eventually, stay-at-home orders did appear to have an effect. Health officials tracked the three-day percent change in cases, and over time, that rate began to decline—even though the total number of cases kept rising dramatically.
How Community Mitigation Works
The CDC’s report relied on anonymized cell phone location data from SafeGraph, a company that tracks movement patterns. During the pandemic, SafeGraph collected data on devices that moved more than 150 meters from their usual nighttime locations. For example, if your phone stays on your bedside table at night and then moves 150 meters away during the day, they assume you’ve left home and aren’t sheltering in place.
The CDC recommends a range of mitigation strategies, including limiting large gatherings, closing schools, restricting business operations, and issuing shelter-in-place orders. However, the data on how effective these policies are in reducing actual case numbers is still limited. While there appears to be a correlation between policy implementation and reduced mobility, causality hasn’t been proven yet. Additionally, we still don’t have a full picture of how many people are infected, due to ongoing testing limitations.
It’s also important to note that behavior may differ significantly between urban centers like Seattle or New York and rural areas like Alabama or Wyoming. These differences could affect how well mitigation strategies work in different regions.
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