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4. How does your technology shift power?

As an example, see this paper on the privacy implications of contact tracing and the authors’ explicit statement of how their ideas should and should not be used. In many cases, your technology’s limitations mean it should not influence policy decisions; state this up front and repeat it as necessary. 

these spaces often obscure the voices of the most vulnerable—including communities without access to technology; people who are unhoused, in nursing homes or in prisons; and those who cannot speak freely. Find people and organizations that center vulnerable communities. Listen carefully. What do they think is most pressing? Do they want you to build your technology for them, with them, or not at all?

1. Are you listening to experts and vulnerable communities?

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.

Even more granular examples, such as footfalls at identifiable business locations, risks de-identifying religious groups; patients visiting cancer hospitals, HIV clinics, or reproductive health clinics; or those seeking public assistance. The medical and public health communities long ago deemed the un-masking of such information without consent unacceptable, but companies have recently been releasing it on publicly available dashboards.

Until we know more about how these changing movement patterns impact epidemiological aspects of the disease, we should use these data with caution.

Simply presenting them, or interpreting them without a proper contextual understanding, could inadvertently lead to imposing or relaxing restrictions on lives and livelihoods, based on incomplete information.

In the absence of a tightly coupled testing and treatment plan, however, these apps risk either providing false reassurance to communities where infectious but asymptomatic individuals can continue to spread disease, or requiring an unreasonably large number of people to quarantine. The behavioral response of the population to these apps is therefore unknown and likely to vary significantly across societies.

Some contact-tracing apps follow black-box algorithms, which preclude the global community of scientists from refining them or adopting them elsewhere. These non-transparent, un-validated interventions — which are now being rolled out (or rolled back) in countries such as China, India, Israel and Vietnam — are in direct contravention to the open cross-border collaboration that scientists have adopted to address the Covid-19 pandemic.

pidemiological models that can help predict the burden and pattern of spread of Covid-19 rely on a number of parameters that are, as yet, wildly uncertain. We still lack many of the basic facts about this disease, including how many people have symptoms, whether people who have been infected are immune to reinfection, and — crucially — how many people have been infected so far. In the absence of reliable virological testing data, we cannot fit models accurately, or know confidently what the future of this epidemic will look like for all these reasons, and yet numbers are being presented to governments and the public with the appearance of certainty

Telenor, the Norwegian telco giant has led the way in responsible use of aggregated mobility data from cell phone tower records. Its data have been used, in close collaboration with scientists and local practitioners, to model, predict, and respond to outbreaks around the world. Telenor has openly published its methods and provided technological guidance on how telco data can be used in public health emergencies in a responsible, anonymized format that does not risk de-identification.

The Covid-19 Mobility Data Network, of which we are part, comprises a voluntary collaboration of epidemiologists from around the world analyzes aggregated data from technology companies to provide daily insights to city and state officials from California to Dhaka, Bangladesh.

The insights are created with aggregated, anonymized sets of data from users who have turned on the Location History setting, which is off by default.

A1 Telekom Austria Group, the country’s largest mobile phone company, is sharing results from a motion analysis application developed by Invenium, a spin-off from the Graz University of Technology that it has backed.

Invenium analyses how flows of people affect traffic congestion or how busy a tourist site will get, said co-founder Michael Cik, but its technology is equally applicable to assessing the effectiveness of measures to reduce social contact or movement that seek to contain an epidemic.

As to privacy, Kassai says data in Onfido's system would be stored on “a private server for an individual,” that can only be accessed with the user's face. "No private business, no government should really be, there's no need for them to hold your personal data. You as a consumer should," he says.