Among the pandemic’s biggest challenges for public health experts have been just how novel it is, how hard it’s been to come by sufficient useful data, and how few tools scientists have for accurately tracking and predicting its spread.
A recent study looking at information gathered by an app that 500,000 people use to log daily symptoms, health status, and exposures to COVID-19 hints at the possible role crowdsourced big data can play in understanding and predicting the spread of infection.
The analysis looked at self-reported data from the How We Feel app collected during April and May to determine which populations were likeliest to have been tested for the virus, the prevalence of social distancing and mask wearing, and what factors were most associated with people who tested positive in that period, such as key symptoms, exposure risks, preexisting medical conditions, and demographic information.
The study showed that Black and Latinx users, frontline health care workers, and essential workers had double the risk for infection than other groups after adjusting for social-economic and preexisting medical conditions, and that those same groups, along with people who were symptomatic, were likelier than others to be tested during April and May.
According to the researchers, this was a double-edged sword, because while it meant sick people were being tested, it also meant asymptomatic cases were likely being missed due to strict testing guidelines that called for only those with symptoms to be checked. The team also found that 36 percent of app users who tested positive reported symptoms not listed by the Centers for Disease Control during the April-May timeframe, or had no symptoms at all.
“The first message from