Mobile phone data and COVID-19: Missing an opportunity?


Paper by Nuria Oliver, et al: “This paper describes how mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in assessing the effectiveness of control measures such as physical distancing. It identifies key gaps and reasons why this kind of data is only scarcely used, although their value in similar epidemics has proven in a number of use cases. It presents ways to overcome these gaps and key recommendations for urgent action, most notably the establishment of mixed expert groups on national and regional level, and the inclusion and support of governments and public authorities early on. It is authored by a group of experienced data scientists, epidemiologists, demographers and representatives of mobile network operators who jointly put their work at the service of the global effort to combat the COVID-19 pandemic….(More)”.

Data Protection under SARS-CoV-2


GDPR Hub: “The sudden outbreak of cases of COVID-19-afflictions (“Corona-Virus”), which was declared a pandemic by the WHO affects data protection in various ways. Different data protection authorities published guidelines for employers and other parties involved in the processing of data related to the Corona-Virus (read more below).

The Corona-Virus has also given cause to the use of different technologies based on data collection and other data processing activities by the EU/EEA member states and private companies. These processing activities mostly focus on preventing and slowing the further spreading of the Corona-Virus and on monitoring the citizens’ abidance with governmental measures such as quarantine. Some of them are based on anonymous or anonymized data (like for statistics or movement patterns), but some proposals also revolved around personalized tracking.

At the moment, it is not easy to figure out, which processing activities are actually supposed to be conducted and which are only rumors. This page will therefore be adapted once certain processing activities have been confirmed. For now, this article does not assess the lawfulness of particular processing activities, but rather outlines the general conditions for data processing in connection with the Corona-Virus.

It must be noted that several activities – such as monitoring, if citizens comply with quarantine and stay indoors by watching at mobile phone locations – can be done without having to use personal data under Article 4(1) GDPR, if all necessary information can be derived from anonymised data. The GDPR does not apply to activities that only rely on anonymised data….(More)”.

Why isn’t the government publishing more data about coronavirus deaths?


Article by Jeni Tennison: “Studying the past is futile in an unprecedented crisis. Science is the answer – and open-source information is paramount…Data is a necessary ingredient in day-to-day decision-making – but in this rapidly evolving situation, it’s especially vital. Everything has changed, almost overnight. Demands for foodtransport, and energy have been overhauled as more people stop travelling and work from home. Jobs have been lost in some sectors, and workers are desperately needed in others. Historic experience can no longer tell us how our society or economy is working. Past models hold little predictive power in an unprecedented situation. To know what is happening right now, we need up-to-date information….

This data is also crucial for scientists, who can use it to replicate and build upon each other’s work. Yet no open data has been published alongside the evidence for the UK government’s coronavirus response. While a model that informed the US government’s response is freely available as a Google spreadsheet, the Imperial College London model that prompted the current lockdown has still not been published as open-source code. Making data open – publishing it on the web, in spreadsheets, without restrictions on access – is the best way to ensure it can be used by the people who need it most.

There is currently no open data available on UK hospitalisation rates; no regional, age or gender breakdown of daily deaths. The more granular breakdown of registered deaths provided by the Office of National Statistics is only published on a weekly basis, and with a delay. It is hard to tell whether this data does not exist or the NHS has prioritised creating dashboards for government decision makers rather than informing the rest of the country. But the UK is making progress with regard to data: potential Covid-19 cases identified through online and call-centre triage are now being published daily by NHS Digital.

Of course, not all data should be open. Singapore has been publishing detailed data about every infected person, including their age, gender, workplace, where they have visited and whether they had contact with other infected people. This can both harm the people who are documented and incentivise others to lie to authorities, undermining the quality of data.

When people are concerned about how data about them is handled, they demand transparency. To retain our trust, governments need to be open about how data is collected and used, how it’s being shared, with whom, and for what purpose. Openness about the use of personal data to help tackle the Covid-19 crisis will become more pressing as governments seek to develop contact tracing apps and immunity passports….(More)”.

Urgently Needed for Policy Guidance: An Operational Tool for Monitoring the COVID-19 Pandemic


Paper by Stephane Luchini et al:” The radical uncertainty around the current COVID19 pandemics requires that governments around the world should be able to track in real time not only how the virus spreads but, most importantly, what policies are effective in keeping the spread of the disease under check. To improve the quality of health decision-making, we argue that it is necessary to monitor and compare acceleration/deceleration of confirmed cases over health policy responses, across countries. To do so, we provide a simple mathematical tool to estimate the convexity/concavity of trends in epidemiological surveillance data. Had it been applied at the onset of the crisis, it would have offered more opportunities to measure the impact of the policies undertaken in different Asian countries, and to allow European and North-American governments to draw quicker lessons from these Asian experiences when making policy decisions. Our tool can be especially useful as the epidemic is currently extending to lower-income African and South American countries, some of which have weaker health systems….(More)”.

Privacy Protection Key for Using Patient Data to Develop AI Tools


Article by  Jessica Kent: “Clinical data should be treated as a public good when used for research or artificial intelligence algorithm development, so long as patients’ privacy is protected, according to a report from the Radiological Society of North America (RSNA).

As artificial intelligence and machine learning are increasingly applied to medical imaging, bringing the potential for streamlined analysis and faster diagnoses, the industry still lacks a broad consensus on an ethical framework for sharing this data.

“Now that we have electronic access to clinical data and the data processing tools, we can dramatically accelerate our ability to gain understanding and develop new applications that can benefit patients and populations,” said study lead author David B. Larson, MD, MBA, from the Stanford University School of Medicine. “But unsettled questions regarding the ethical use of the data often preclude the sharing of that information.”

To offer solutions around data sharing for AI development, RSNA developed a framework that highlights how to ethically use patient data for secondary purposes.

“Medical data, which are simply recorded observations, are acquired for the purposes of providing patient care,” Larson said….(More)”

Coronavirus Innovation Map


The Coronavirus Innovation Map is a platform of hundreds of innovations and solutions from around the world that help people cope and adapt to life amid the coronavirus pandemic, and to connect innovators.

The CoronaVirus Innovation Map is a visualized global database that is mapping the innovations related to tackling coronavirus in various fields such as diagnostics, treatment, lifestyle changes, etc., on a geographical scale….

Our goal with the Coronavirus Innovation Map is to build a crowdsourced resource that maps hundreds of innovations and solutions globally that help people cope and adapt to life amid the coronavirus, and to connect innovators.

This platform is a database for innovators to know who the other players are and where the projects or startups are located allowing them to connect and create solutions in this field. Policymakers will also be able to efficiently look for viable solutions in one place.

You may use the map to browse initiatives in specific locations (type a city or country in the search box), or choose a category wherein you would like to find a solution….(More)”

Responding to COVID-19 with AI and machine learning


Paper by Mihaela van der Schaar et al: “…AI and machine learning can use data to make objective and informed recommendations, and can help ensure that scarce resources are allocated as efficiently as possible. Doing so will save lives and can help reduce the burden on healthcare systems and professionals….

1. Managing limited resources

AI and machine learning can help us identify people who are at highest risk of being infected by the novel coronavirus. This can be done by integrating electronic health record data with a multitude of “big data” pertaining to human-to-human interactions (from cellular operators, traffic, airlines, social media, etc.). This will make allocation of resources like testing kits more efficient, as well as informing how we, as a society, respond to this crisis over time….

2. Developing a personalized treatment course for each patient 

As mentioned above, COVID-19 symptoms and disease evolution vary widely from patient to patient in terms of severity and characteristics. A one-size-fits-all approach for treatment doesn’t work. We also are a long way off from mass-producing a vaccine. 

Machine learning techniques can help determine the most efficient course of treatment for each individual patient on the basis of observational data about previous patients, including their characteristics and treatments administered. We can use machine learning to answer key “what-if” questions about each patient, such as “What if we postpone a couple hours before putting them on a ventilator?” or “Would the outcome for this patient be better if we switched them from supportive care to an experimental treatment earlier?”

3. Informing policies and improving collaboration

…It’s hard to get a clear sense of which decisions result in the best outcomes. In such a stressful situation, it’s also hard for decision-makers to be aware of the outcomes of decisions being made by their counterparts elsewhere. 

Once again, data-driven AI and machine learning can provide objective and usable insights that far exceed the capabilities of existing methods. We can gain valuable insight into what the differences between policies are, why policies are different, which policies work better, and how to design and adopt improved policies….

4. Managing uncertainty

….We can use an area of machine learning called transfer learning to account for differences between populations, substantially eliminating bias while still extracting usable data that can be applied from one population to another. 

We can also use methods to make us aware of the degree of uncertainty of any given conclusion or recommendation generated from machine learning. This means that decision-makers can be provided with confidence estimates that tell them how confident they can be about a recommended course of action.

5. Expediting clinical trials

Randomized clinical trials (RCTs) are generally used to judge the relative effectiveness of a new treatment. However, these trials can be slow and costly, and may fail to uncover specific subgroups for which a treatment may be most effective. A specific problem posed by COVID-19 is that subjects selected for RCTs tend not to be elderly, or to have other conditions; as we know, COVID-19 has a particularly severe impact on both those patient groups….

The AI and machine learning techniques I’ve mentioned above do not require further peer review or further testing. Many have already been implemented on a smaller scale in real-world settings. They are essentially ready to go, with only slight adaptations required….(More) (Full Paper)”.

Doctors Turn to Social Media to Develop Covid-19 Treatments in Real Time


Michael Smith and Michelle Fay Cortez at Bloomberg: “There is a classic process for treating respiratory problems: First, give the patient an oxygen mask, or slide a small tube into the nose to provide an extra jolt of oxygen. If that’s not enough, use a “Bi-Pap” machine, which pushes air into the lungs more forcefully. If that fails, move to a ventilator, which takes over the patient’s breathing.

But these procedures tend to fail With Covid-19 patients. Physicians found that by the time they reached that last step, it was often too late; the patient was already dying.

In past pandemics like the 2003 global SARS outbreak, doctors sought answers to such mysteries from colleagues in hospital lounges or maybe penned articles for medical journals. It could take weeks or months for news of a breakthrough to reach the broader community.

For Covid-19, a kind of medical hive mind is on the case. By the tens of thousands, doctors are joining specialized social media groups to develop answers in real time. One of them, a Facebook group called the PMG COVID19 Subgroup, has 30,000 members worldwide….

Doctors are trying to fill an information void online. Sabry, an emergency-room doctor in two hospitals outside Los Angeles, found that the 70,000-strong, Physician Moms Group she started five years ago on Facebook was so overwhelmed by coronavirus threads that she created the Covid-19 offshoot. So many doctors tried to join the new subgroup that Facebook’s click-to-join code broke. Some 10,000 doctors waited in line as the social media company’s engineers devised a fix.

She’s not alone. The topic also consumed two Facebook groups started by Dr. Nisha Mehta, a 38-year-old radiologist from Charlotte, North Carolina. The 54,000-member Physician Side Gigs, intended for business discussions, and an 11,000-person group called Physician Community for more general topics, are also all coronavirus, all the time, with thousands waiting to join…(More)”.

A Closer Look at Location Data: Privacy and Pandemics


Assessment by Stacey Gray: “In light of COVID-19, there is heightened global interest in harnessing location data held by major tech companies to track individuals affected by the virus, better understand the effectiveness of social distancing, or send alerts to individuals who might be affected based on their previous proximity to known cases. Governments around the world are considering whether and how to use mobile location data to help contain the virus: Israel’s government passed emergency regulations to address the crisis using cell phone location data; the European Commission requested that mobile carriers provide anonymized and aggregate mobile location data; and South Korea has created a publicly available map of location data from individuals who have tested positive. 

Public health agencies and epidemiologists have long been interested in analyzing device location data to track diseases. In general, the movement of devices effectively mirrors movement of people (with some exceptions discussed below). However, its use comes with a range of ethical and privacy concerns. 

In order to help policymakers address these concerns, we provide below a brief explainer guide of the basics: (1) what is location data, (2) who holds it, and (3) how is it collected? Finally we discuss some preliminary ethical and privacy considerations for processing location data. Researchers and agencies should consider: how and in what context location data was collected; the fact and reasoning behind location data being classified as legally “sensitive” in most jurisdictions; challenges to effective “anonymization”; representativeness of the location dataset (taking into account potential bias and lack of inclusion of low-income and elderly subpopulations who do not own phones); and the unique importance of purpose limitation, or not re-using location data for other civil or law enforcement purposes after the pandemic is over….(More)”.

Data Collaboratives in Response to COVID19


Living Repository: “This document is part of a call for action to build a responsible infrastructure for data-driven pandemic response. 

It serves as a living repository for data collaboratives seeking to address the spread of COVID-19 and its secondary effects. 

> You can find ongoing data collaborative projects here

> Requests for data and expertise that might lead to data collaboratives can be found here.

> Data competitions, challenges, and calls for proposals, which can lead to useful tools to combat COVID-19, can be found here.

The repository aims to include projects that show a commitment to privacy protection, data responsibility, and overall user well-being. 

It will be updated regularly as we receive projects and proposals or otherwise become aware of them. 

HELP US MAKE THIS REPOSITORY BETTER:  Individuals are encouraged to edit the repo and/or suggest additions to this document if a project is not currently listed.

See full Living Repository here.