In the early days of the coronavirus pandemic, many people were excited by the idea that technology could help us track and contain the virus’s spread. Singapore was one of the first to release a digital contact tracing app. Countries from Iceland to Australia soon followed, while Apple and Google built a platform that allows their phones to be used for contact tracing. The apps track who you’ve come into close contact with, using either GPS or a “digital handshake” between phones over Bluetooth. If a user gets Covid-19, they alert the app, and anyone who has been near them in recent days is notified that they may have been exposed.
So far, contact tracing apps don’t seem to be doing much to stop the spread of the virus. Researchers at Oxford University estimated that 60 percent of the population would need to use a contact tracing app for it to be effective, and no country has come close to that threshold. Does this mean we should give up on these apps?
Kimon Drakopoulos is an assistant professor of data sciences and operations at USC Marshall School of Business, where he researches complex networked systems. He completed his PhD at MIT. He is currently working on a project with the Greek government that combines real-time data and machine learning to identify high-risk tourists and target coronavirus testing resources.
As a data scientist who studies how contagions like the flu spread across networks and how epidemics can be controlled, I believe we’ve been thinking about contact tracing apps all wrong. Telling individuals if they may have been exposed to the virus is important, of course. But the larger value of these apps lies in the real-time data they can provide decisionmakers about people’s behavior, revealing the bigger picture of how many potential exposures are happening in a community every day and where they are occurring.
We don’t just want an app to tell us, days later, if we’ve encountered someone with Covid-19. We want to know how many people each of us encounters over the course of a day—10? 50? 100?—any of whom could have the virus. That one simple data point—the average number of interactions (with anyone, not just those who have tested positive), and therefore potential exposures, a person has per day—can help our leaders make smarter decisions about when and how to reopen.
Network science teaches us that fighting an epidemic is like battling a fire. You have to respond quickly and contain the fire at its edges so it doesn’t spread. If we eased up and left the scene once we thought we had a fire under control, but didn’t know for weeks if it had started spreading again, by the time we had our answers it would be far too late. We need to watch the fire in real time and target our resources to the border areas where it’s about to spill over.
Right now decisionmakers are flying blind as they try to fight this fire. When communities lift lockdowns, they’re basically conducting experiments that take weeks to deliver results, potentially losing lives in the process. States like Florida, Texas, and Arizona started reopening in April and May but didn’t see the fallout from their decisions until June and July. The virus was escalating on the ground for a long time before it began to show up in official case counts, hospitalizations, and now deaths.
How can other states avoid becoming the next Florida? They can use contact tracing apps to gather real-time data about which activities or locations might be responsible for a dangerously high number of potential exposures. Say your state reopens restaurants for indoor dining. Does this double the number of interactions the average person has each day, or increase it by 20? The answer to that question makes a huge difference in deciding whether reopening restaurants is the right move, but currently we have no way of knowing. These apps can help us track the real-time consequences of policy decisions about when and how to reopen schools, parks, shops, offices, and other spaces.
Data from contact tracing apps can also help us better target interventions by revealing where exposures are happening (for privacy reasons, this would be at a neighborhood, block, or zip code level, not individual locations). If a specific public beach or park is a hot spot for interactions, for example, maybe it should be closed while other less-trafficked spots remain open. If the average resident in Town A has three face-to-face interactions a day while the average person in Town B has 25, depending on the local infection rates, maybe it’s time to open up Town A while directing additional testing resources to Town B. This helps us move from blanket state- or citywide lockdowns to a more targeted response.
Using data in this way avoids one of the biggest problems with contact tracing apps so far—that not many people are using them. If only one out of 10 people you encounter at the gym has the app, it won’t alert you if you were exposed to coronavirus by one of the other nine. But aggregate, community-wide data about people’s level of interactions can be useful to policymakers even if usage rates are low, because statistical techniques from data science allows us to estimate overall exposure trends. If we combine real-time data with artificial intelligence, as my colleagues and I are currently helping the Greek government do, we can target testing and other resources even more effectively. A rate as small as 20 percent, with fair representation from all age groups and locations, would be enough to inform targeted interventions.
If officials are worried about bias in the data, they can encourage people in underrepresented areas of the community to participate through public health campaigns or financial incentives. We may also be able to get more people to use these apps by launching them community by community or through institutions like schools, companies, churches, sports leagues, and neighborhood associations. As some universities reopen in the fall and continue to adjust what life will be like on campus, they could be an ideal environment for testing contact tracing apps and how their data can be used to inform policy decisions.
User uptake isn’t a major obstacle, but what about privacy? Because it focuses on aggregate data across a community, everything I’ve described can be done without collecting individually identifying data about specific people or locations. Not to mention that this data collection would be less invasive than many of the apps we currently have on our phones for much more frivolous reasons.
We can guard people’s privacy if the apps are designed and used properly, but so far some have fallen short. Recent surveys found that privacy is a major obstacle to getting people to use contact tracing apps, and that there’s a lot of confusion about how these apps work and how much data they expose. A bipartisan bill introduced in Congress last month would ensure proper privacy protections are in place for contact tracing apps. This kind of regulation is absolutely essential, as is communication with the public about what data these apps do and don’t collect and how it is used.
Last week, Virginia became the first state in the US to launch an app using the most prominent contact tracing platform built by Google and Apple. However, only three other states are readying similar apps and, according to a survey by Business Insider, most states say they have no plans to develop apps like these for their residents. I believe this is in part due to a misunderstanding about what a powerful tool contact tracing apps can be in our ongoing efforts to balance public health and economic well-being.
If used to track community-level trends, not just individual exposures, these apps could liberate us from the false binary that we have to either stay in lockdown or open up and simply hope for the best. Better data would allow us to stop flying blind and instead adapt our response in real time as the threat evolves, charting a middle path that is more efficient and less economically damaging.
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