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In ​India​, at least two state governments — including the state of Karnataka, housing the tech hub of Bangalore — have uploaded PDF files online with names, house addresses, and travel history of people ordered into COVID-19 quarantines. The 12 information is accessible by everyone.

In particular, the ongoing crisis highlights how much the public and public authorities are depending on tech companies to function: from providing broadband access, to allowing people to work from home, to providing video-conferencing solutions or tools that respond directly to the crisis, such as diagnosis apps.

In ​Tunisia​,​ ​Enova Robotics signed an agreement with the Ministry of Interior to start operating PGuard robots. These robots will be equipped with a set of infrared 39 cameras and used to stop people from leaving their houses. There is no information as to where these robots will be deployed, what information they will gather, how long they will keep the data and who would have access to it.

Government and public health authorities broadly raise questions in at least four critical areas of inquiries for which the use of mobile phone data is relevant. First, situational awareness questions seek to develop an understanding of the dynamic environment of the pandemic. Mobile phone data can provide access to previously unavailable population estimates and mobility information to enable stakeholders across sectors better understand COVID-19 trends and geographic distribution. Second, cause-and-effect questions seek to help identify the key mechanisms and consequences of implementing different measures to contain the spread of COVID-19. They aim to establish which variables make a difference for a problem and whether further issues might be caused. Third, predictive analysis seeks to identify the likelihood of future outcomes and could, for example, leverage real-time population counts and mobility data to enable predictive capabilities and allow stakeholders to assess future risks, needs, and opportunities. Finally, impact assessments aim to determine which, whether, and how various interventions affect the spread of COVID-19 and require data to identify the obstacles hampering the achievement of certain objectives or the success of particular interventions.

During the acceleration phase, when community transmission reaches exponential levels, the focus is on interventions for containment, which typically involve social contact and mobility restrictions. At this stage, aggregated mobile phone data are valuable to assess the efficacy of implemented policies through the monitoring of mobility between and within affected municipalities. Mobility information also contributes to the building of more accurate epidemiological models that can explain and anticipate the spread of the disease, as shown for H1N1 flu outbreaks (29). These models, in turn, can inform the mobilization of resources (e.g., respirators and intensive care units).

Continued situational monitoring will be important as the COVID-19 pandemic is expected to come in waves (4, 31). Near real-time data on mobility and hotspots will be important to understand how lifting and reestablishing various measures translate into behavior, especially to find the optimal combination of measures at the right time (e.g., general mobility restrictions, school closures, and banning of large gatherings), and to balance these restrictions with aspects of economic vitality.

After the pandemic has subsided, mobile data will be helpful for post hoc analysis of the impact of different interventions on the progression of the disease and cost-benefit analysis of mobility restrictions. During this phase, digital contact-tracing technologies might be deployed, such as the Korean smartphone app Corona 100m (32) and the Singaporean smartphone app TraceTogether (33), that aim at minimizing the spread of a disease as mobility restrictions are lifted.

Origin-destination (OD) matrices are especially useful in the first epidemiological phases, where the focus is to assess the mobility of the population. The number of people moving between two different areas daily can be computed from the mobile network data, and it can be considered a proxy of human mobility.

Amount of time spent at home, at work, or other locations are estimates of the individual percentage of time spent at home/work/other locations (e.g., public parks, malls, and shops), which can be useful to assess the local compliance with countermeasures adopted by governments. The home and work locations need to be computed in a period of time before the deployment of mobility restrictions measures.

The use of mobile phone data for tackling the COVID-19 pandemic has gained attention but remains relatively scarce.

First, governments and public authorities frequently are unaware and/or lack a “digital mindset” and capacity needed for both for processing information that often is complex and requires multidisciplinary expertise (e.g., mixing location and health data and specialized modeling) and for establishing the necessary interdisciplinary teams and collaborations. Many government units are understaffed and sometimes also lack technological equipment.

Second, despite substantial efforts, access to data remains a challenge. Most companies, including mobile network operators, tend to be very reluctant to make data available—even aggregated and anonymized—to researchers and/or governments. Apart from data protection issues, such data are also seen and used as commercial assets, thus limiting the potential use for humanitarian goals if there are no sustainable models to support operational systems. One should also be aware that not all mobile network operators in the world are equal in terms of data maturity. Some are actively sharing data as a business, while others have hardly started to collect and use data.

Third, the use of mobile phone data raises legitimate public concerns about privacy, data protection, and civil liberties.

Control of the pandemic requires control of people—including their mobility and other behaviors. A key concern is that the pandemic is used to create and legitimize surveillance tools used by government and technology companies that are likely to persist beyond the emergency. Such tools and enhanced access to data may be used for purposes such as law enforcement by the government or hypertargeting by the private sector. Such an increase in government and industry power and the absence of checks and balance is harmful in any democratic state. The consequences may be even more devastating in less democratic states that routinely target and oppress minorities, vulnerable groups, and other populations of concern.

Fourth, researchers and technologists frequently fail to articulate their findings in clear, actionable terms that respond to practical political and technical questions. Researchers and domain experts tend to define the scope and direction of analytical problems from their perspective and not necessarily from the perspective of governments’ needs. Critical decisions have to be taken, while key results are often published in scientific journals and in jargon that are not easily accessible to outsiders, including government workers and policy makers.

Last, there is little political will and resources invested to support preparedness for immediate and rapid action. On country levels, there are too few latent and standing mixed teams, composed of (i) representatives of governments and public authorities, (ii) mobile network operators and technology companies, and (iii) different topic experts (virologists, epidemiologists, and data analysts); and there are no procedures and protocols predefined. None of these challenges are insurmountable, but they require a clear call for action.

To effectively build the best, most up-to-date, relevant, and actionable knowledge, we call on governments, mobile network operators, and technology companies (e.g. Google, Facebook, and Apple), and researchers to form mixed teams.

For later stages of the pandemic, and for the future, stakeholders should aim for a minimum level of “preparedness” for immediate and rapid action.

Policymakers must be clear about the level of analysis they are seeking, and realistic about the capabilities of technology to achieve this. It is a challenge going from achieving high level location insights on a community level such as a building, neighbourhood or street to an individual level, and governments should be prepared to be told that current data infrastructure doesn’t support exactly what they are asking for. Focusing on community data is currently much easier than focusing on individual data. Issues around precision can be solved by 5G, but we don’t currently have that capability.

Governments must evaluate whether the trade-off they are asking citizens to make is commensurate with the value created. For example, if you are building individual contact tracing and the data is accurate within 1 kilometre, the value of the data is low, and the trade-off may not be worth it. They must also be straightforward with the public about the expected benefits and limitations of the technologies they are pursuing, and the trade-offs with other concerns in relation to privacy and data security.

Governments should work with partners, but they should do so by putting out clear calls for assistance to engage with the right level and type of expertise. So far, the engagement from many governments has happened on an ad-hoc basis, and partnerships between government and companies or researchers has happened as a result of partners approaching government first. Instead, governments must be clear about their objectives from the outset and put out a call for support from technical experts. Mobile operators can help governments analyse data on a community level; working with data can give some false conclusions, which mobile operators can help to address.

The political herd immunity to which governments are prone is that it is much safer to fail with a policy that others are following than to fail with a distinctive policy, even if, ex ante, the chances of failure are higher with the former.