Striking maps highlight how location data could be used to trace coronavirus
Travel data may be a treasure trove for scientists studying Covid-19.
On March 24, a now Twitter-famous data map revealed how spring breakers on a single beach in Fort Lauderdale may have led the novel coronavirus to spread across the United States like wildfire.
Not only were these spring breakers a prime example of pre-quarantine foolishness ("If I get corona, I get corona," anyone?), but their location data puts a spotlight on the fact that these data may tell us how the coronavirus spreads — and pose new solutions to the problem.
The Florida beach location data is just one of several visualizations released by Tectonix GEO, a data visualization group.
These videos graphically illustrate just how important limiting non-essential travel really is when it comes to slowing the spread of Covid-19.
"Want to see the true potential impact of ignoring social distancing," the company said in a tweet accompanying the Fort Lauderdale data. At the time of publication, Tectonix GEO had not responded to requests for comment by Inverse.
These visualizations are striking, but they are also not a scientific study. In fact, depending on whom you ask, they also illustrate just how much anonymized location data can still reveal about an individual's private life.
But two new papers published on Tuesday in the journal Science demonstrate just how revelatory this kind of data can be — and how useful for fighting the coronavirus' spread.
These videos pull those findings into stark focus.
In the first study, scientists build upon earlier evidence that China's massive lockdown (the closure of roads and public transport) of Wuhan, was particularly useful at the outset of the outbreak, but became less useful as the outbreak wore on.
In the first 50 days of Wuhan's outbreak, the travel ban delayed the emergence of Covid-19 cases in 19 other cities by about three days, the results suggest. Cities that implemented similar control measures (closing schools or prohibiting public gathering) reported about 33 percent fewer cases per week during the early days of the outbreak.
The results illuminate the importance of enacting travel policies quickly and preemptively. But once local transmission is established, it's a different story, as Sam Scarpino, the author of the initial study on Wuhan's lockdown told Inverse previously.
"Once local transmission of Covid-19 is established, which is the case throughout the United States, mobility restrictions don't actually help that much in terms of flattening the curve," he said.
Discretionary travel is not recommended here in the United States — the maps provide an all too-salient reminder as to why. But they also can't tell us much about what happens once the outbreak establishes itself in a place, Bill Fulton the director of Rice University's Kinder Institute for Urban Research, says.
"What I’ve seen is mobile phone tracking that measures the overall amount of travel, which is clearly down," he tells Inverse. "I’m not sure the mobile phone data will help us in informing whether we are all standing six feet apart."
Social distancing is widely accepted as a crucial strategy to stop local spread.
Beyond informing early policy decisions, movement data may inform our fight against Covid-19 in another way, the second Science paper suggests.
One form of tracking a disease' spread is known as contact tracing. Epidemiologists use this method to try to figure out who has been exposed to a condition, and who they may have in turn exposed. Usually, this is done by interviewing patients about whom they may have had contact with after they present themselves to a medical facility.
But leveraging the same phone location data could enable digital contact tracing. In that technique, scientists could use your phone data to figure out whether you may have been exposed to a Covid-19 case, and alert you so you can start self-isolating right away.
The researchers on this paper predict that such a measure could help greatly reduce a value known as R0, which is a measure of how many additional people are likely to be infected by each person diagnosed with a disease.
Instant, digital contact tracing through a mobile app would reduce the R0 value to below 1 — the value needed to control the spread, the researchers suggest.
There are limitations to this theory. The study is based on mathematical modeling, not a controlled experiment. And there are obvious privacy concerns about using mobile data to track people.
China is already pushing ahead with this means of contact tracing, however. The authorities there created an app that assigns "color codes" to people based on their self-reported contact histories, and "government background system information," per Xinhua, China's state news media.
These codes have real health consequences. They can determine whether or not one can leave self-quarantine, for example. There is also some evidence that this information is shared with police departments, suggesting that such tool may be misused.
In Korea, there is also concern that such data could be used to reinforce stigma towards people who test positive for the virus, by alerting users to the fact that someone in their vicinity has tested positive.
Ultimately, the privacy concerns should not limit the use of this data entirely, Fulton says. Rather, it needs to be treated properly to inform public health and protect individuals.
"To inform policy, you have to 'anonymize' the data so you don’t know specifically who is traveling. But this can be difficult if you are combining it with Covid data, which is a fairly small number of cases comparatively speaking," he says.
That said, we're already using data on human movement to learn more about the coronavirus. Whether it's something that's worth using to combat it preemptively, is still out for debate.
Abstract, H. Tian et al, Science (2020): Responding to an outbreak of a novel coronavirus (agent of COVID-19) in December 2019, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. We investigated the spread and control of COVID-19 using a unique data set including case reports, human movement and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days (95%CI: 2.54-3.29). Cities that implemented control measures pre-emptively reported fewer cases, on average, in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Suspending intra-city public transport, closing entertainment venues and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).
Abstract, L. Ferretti et al, Science (2020): The newly emergent human virus SARS-CoV-2 is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact-tracing needed to stop the epidemic. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.