One of the first, well-covered COVID-19 digital contact-tracing cases happened in the United States, where authorities used a suspected carrier’s Uber history to track down possible contacts in Mexico. The positive outcome was that the patient was found, but the negative side of the story was that Uber temporarily suspended 240 individuals’ accounts, based on their contact with two drivers whom the platform suspected of being exposed to the virus. Viewed superficially, that’s responsible — but what’s actually happening here is that a private company suspended the livelihood of two drivers and a number of others’ mobility, based on suspicion of exposure, without a whole lot of science.

One of the major risks — both of the platform economy and of the growingly private dimension of disaster response — is that we lose the ability to know whether decisions (such as the one Uber made) were “good.” The way that law usually assesses whether something was “good” is to consider whether a legitimate approach with tested effectiveness was used, whether the actions taken were necessary to achieve the goal, and whether the invasion and harms that the actions cause are proportionate to the size of the problem they solve. Here, for example, Uber clearly thought it was acting in the public’s interest, but its specific approach was to implement a micro travel ban, an approach proved largely ineffective. Ultimately, contact tracing is one of the most legally justifiable uses of sensitive data — but its effectiveness, like its legality, is a product of specific suspicion, scientifically approved testing and institutional response capacity.

Testing and Responder Capacity

Another area where technology is applied in disaster response is to improve, adapt or invest in medical devices, tests, and protective gear. The good news in this area of intervention, which is exceptionally broad, is that when it’s effective, it’s transformative. The bad news, of course, is that a lot of the efforts to change or augment institutional testing capacity do so by reducing the quality control or scientific integrity of the underlying process.

Efforts to improve existing capacity, however, are some of the most positive ways to intervene, because they have comparatively specific problems to solve (say, improving the quality of protective gear), existing pathways to distribution (that is, testing, manufacturing and logistical distribution infrastructure) and users with at least a high-level understanding of the underlying tool. In other words, these efforts, where they are bounded by existing relationships, infrastructure and systems, often do well.

And, as a result, the efforts to ramp up to solve the practical problems surrounding testing for COVID-19 and the creative problems surrounding the need for equipment have been some of the most inspirational parts of the response. For example, South Korea’s use of drive-through testing to limit transmission at health facilities and increase throughput is largely hailed as a success. A range of health services — notably, Ontario’s — are leveraging self-diagnosis and telemedicine to help control the number of patients appearing in health-care facilities. And, while it’s caused some intellectual property litigation, one group of Italian hospital staff is using 3-D printers to try and resolve respirator distribution and manufacturing shortfalls. Using technology in these ways has been specific, limited, and largely created with, and based on, public health institutions’ need. Of course, these innovations are only possible when public institutions understand the transmission of the pathogen and are able to effectively model it based on available data — all of which are still in process for COVID-19.

Early Warning and Surveillance

Early warning and surveillance to better understand the pathogen and monitor the outbreak are critical components of responding to any epidemic, but there are significant differences between disease surveillance and individual surveillance. Disease surveillance focuses on tracking the incidences of the disease and its path, which often coincides with temporarily tracking the people who catch or interact with a disease but, critically, only insofar as absolutely necessary to limit the spread of the virus. Disease surveillance is a critical component of any pandemic response — and COVID-19 has been no exception.

There are significant, positive examples of this type of work. For example, despite deeply political and problematic early reporting, most countries have invested aggressively in testing, openly reporting their caseload and capacities, and are mobilizing rapidly around other critical disease-tracking initiatives. That means that there’s increasingly robust public reporting about the comparative size and scale of the outbreak, for example, the WHO’s daily global situation reports and resource portal and the Centers for Disease Control and Prevention’s updated world map. A number of academic publishers and news outlets have made their content available for free, to help rapidly increase public knowledge and capacity. There are distributed computing efforts, such as [email protected], which helps people to donate their computer processing power to contribute to coronavirus research. Similarly, a number of COVID-19 strains have been genomically sequenced with unprecedented speed — which helps researchers understand, for example, how long the virus may have been in each community and its spread. These efforts play a critical role in helping public health authorities gauge the scope of the epidemic and make recommendations about how to adequately respond.