Using Data for COVID-19 Requires New and Innovative Governance Approaches

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst and Andrew Zahuranec at Data & Policy blog: “There has been a rapid increase in the number of data-driven projects and tools released to contain the spread of COVID-19. Over the last three months, governments, tech companies, civic groups, and international agencies have launched hundreds of initiatives. These efforts range from simple visualizations of public health data to complex analyses of travel patterns.

When designed responsibly, data-driven initiatives could provide the public and their leaders the ability to be more effective in addressing the virus. The Atlantic andNew York Times have both published work that relies on innovative data use. These and other examples, detailed in our #Data4COVID19 repository, can fill vital gaps in our understanding and allow us to better respond and recover to the crisis.

But data is not without risk. Collecting, processing, analyzing and using any type of data, no matter how good intention of its users, can lead to harmful ends. Vulnerable groups can be excluded. Analysis can be biased. Data use can reveal sensitive information about people and locations. In addressing all these hazards, organizations need to be intentional in how they work throughout the data lifecycle.

Decision Provenance: Documenting decisions and decision makers across the Data Life Cycle

Unfortunately the individuals and teams responsible for making these design decisions at each critical point of the data lifecycle are rarely identified or recognized by all those interacting with these data systems.

The lack of visibility into the origins of these decisions can impact professional accountability negatively as well as limit the ability of actors to identify the optimal intervention points for mitigating data risks and to avoid missed use of potentially impactful data. Tracking decision provenance is essential.

As Jatinder Singh, Jennifer Cobbe, and Chris Norval of the University of Cambridge explain, decision provenance refers to tracking and recording decisions about the collection, processing, sharing, analyzing, and use of data. It involves instituting mechanisms to force individuals to explain how and why they acted. It is about using documentation to provide transparency and oversight in the decision-making process for everyone inside and outside an organization.

Toward that end, The GovLab at NYU Tandon developed the Decision Provenance Mapping. We designed this tool for designated data stewards tasked with coordinating the responsible use of data across organizational priorities and departments….(More)”

How Covid-19 Is Accelerating the Rise of Digital Democracy

Curated on Posted on by Stefaan Verhulst

Blog post by Rosie Beacon: “Covid-19 has created an unprecedented challenge for parliaments and legislatures. Social distancing and restrictions on movement have forced parliaments to consider new methods of scrutiny, debate, and voting. The immediate challenge was simply to replicate existing procedures remotely, but the crisis has presented a unique window of opportunity to innovate.

As policymakers slowly transition back to “normal”, they should not easily dismiss the potential of this new relationship between democracy and technology. Parliamentarians should use what they’ve learned and the expertise of the democracy tech and deliberative democracy community to build greater trust in public institutions and open up traditional processes to wider deliberation, bringing people closer to the source of democratic power.

This note sets out some of the most interesting examples of crisis-led parliamentary innovation from around the world and combines it with some of the lessons we already know from democracy and deliberative tech to chart a way forward.

There are five core principles political leaders should embrace from this great experiment in digital parliamentary democracy:

  1. Discover and adopt: The world’s parliaments and legislatures have been through the same challenge. This is an opportunity to learn and improve democratic engagement in the long-term.
  2. Experiment with multiple tools: There is no one holistic approach to applying digital tools in any democracy. Some will work, others will fail – technology does not promise infallibility.
  3. Embrace openness: Where things can be open, experiment with using this to encourage open dialogue and diversify ideas in the democratic and representative process.
  4. Don’t start from scratch: Learn how the deliberative democracy community is already using technology to help remake representative systems and better connect to communities.
  5. Use multi-disciplinary approaches: Create diverse teams, with diverse skill sets. Build flexible tools that meet today’s needs of democracies, citizens and representatives.

Approaches From Around the World

The approaches globally to Covid-19 continuity have been varied depending on the geographical, political and social context, but they generally follow one of these scenarios:

  1. Replicating everything using digital tools – Welsh Assembly, Crown dependencies (Jersey, Isle of Man),Brazil
    • Using technology in every way possible to continue the current parliamentary agenda online.
  2. Moving priority processes online, deprioritising the rest – France National Assembly,New Zealand, Canada
    • No physical presence in parliaments and prioritising the most important elements of the current parliamentary agenda, usually Covid-19-related legislation, to adapt for online continuation.
  3. Shifting what you can online while maintaining a minimal physical parliament – Denmark, Germany, UK
    • Hybrid parliaments appear to be a popular choice for larger parliaments. This generally allows for the parliamentary agenda to continue with amendments to how certain procedures are conducted.
  4. Reducing need for physical attendance and moving nothing online – Ireland, Sweden
    • Houses can continue to sit in quorum (an agreed proportion of MPs representative of overall party representation), but certain parts of legislative agenda have been suspended for the time being….(More)”.

Research methods to consider in a pandemic

Curated on Posted on by Stefaan Verhulst

Blog by Helen Kara: “Since lockdown began, researchers have been discussing how best to change our methods. Of the ‘big three’ – questionnaires, interviews, and focus groups – only questionnaires are still being used in much the same way. There are no face-to-face interviews or focus groups, though interviews can still be held by telephone and both can be done online. However, doing research online comes with new ethical problems. Some organisations are forbidding the use of Zoom because it has had serious security problems, others are promoting the use of Jitsi because it is open source.

I’ve been thinking about appropriate methods and I have come up with three options I think are particularly worth considering at this time: documentary research, autoethnography, and digital methods. These are all comparatively new approaches and each offers scope for considerable creativity. Documentary research seems to be the oldest; I understand that its first textbook, A Matter of Record by UK academic John Scott, was published in 1990. Autoethnography was devised by US academic Carolyn Ellis in the 1990s, and digital methods have developed as technological devices have become more available to more people through the 21st century….

Doing research in a pandemic also requires considerable thought about ethics. I have long argued that ethical considerations should start at the research question, and I believe that is even more crucial at present. Does this research need doing – or does it need doing now, in the middle of a global collective trauma? If not, then don’t do that research, or postpone it until life is easier. Alternatively, you may be doing urgent research to help combat COVID19, or important research that will go towards a qualification, or have some other good reason. In which case, fine, and the next ethical question is: how can my research be done in a way that places the least burden on others? The methods introduced above all offer scope for conducting empirical research without requiring much input from other people. Right now, everyone is upset; many are worried about their health, income, housing, and/or loved ones; increasing numbers are recently bereaved. Therefore everyone is vulnerable, and so needs more care and kindness than usual. This includes potential participants and it also includes researchers. We need to choose our methods with great care for us all….(More)”.

Stay alert, infodemic, Black Death: the fascinating origins of pandemic terms

Curated on Posted on by Stefaan Verhulst

Simon Horobin at The Conversation: “Language always tells a story. As COVID-19 shakes the world, many of the words we’re using to describe it originated during earlier calamities – and have colourful tales behind them.

In the Middle Ages, for example, fast-spreading infectious diseases were known as plagues – as in the Bubonic plague, named for the characteristic swellings (or buboes) that appear in the groin or armpit. With its origins in the Latin word plaga meaning “stroke” or “wound”, plague came to refer to a wider scourge through its use to describe the ten plagues suffered by the Egyptians in the biblical book of Exodus.

An alternative term, pestilence, derives from Latin pestis (“plague”), which is also the origin of French peste, the title of the 1947 novel by Albert Camus (La Peste, or The Plague) which has soared up the bestseller charts in recent weeks. Latin pestis also gives us pest, now used to describe animals that destroy crops, or any general nuisance or irritant. Indeed, the bacterium that causes Bubonic plague is called Yersinia pestis….

The later plagues of the 17th century led to the coining of the word epidemic. This came from a Greek word meaning “prevalent”, from epi “upon” and demos “people”. The more severe pandemic is so called because it affects everyone (from Greek pan “all”).

A more recent coinage, infodemic, a blend of info and epidemic, was introduced in 2003 to refer to the deluge of misinformation and fake news that accompanied the outbreak of SARS (an acronym formed from the initial letters of “severe acute respiratory syndrome”).

The 17th-century equivalent of social distancing was “avoiding someone like the plague”. According to Samuel Pepys’s account of the outbreak that ravaged London in 1665, infected houses were marked with a red cross and had the words “Lord have mercy upon us” inscribed on the doors. Best to avoid properties so marked….(More)”.

Open science: after the COVID-19 pandemic there can be no return to closed working

Curated on Posted on by Stefaan Verhulst

Article by Virginia Barbour and Martin Borchert: “In the few months since the first case of COVID-19 was identified, the underlying cause has been isolated, its symptoms agreed on, its genome sequenced, diagnostic tests developed, and potential treatments and vaccines are on the horizon. The astonishingly short time frame of these discoveries has only happened through a global open science effort.

The principles and practices underpinning open science are what underpin good research—research that is reliable, reproducible, and has the broadest impact possible. It specifically requires the application of principles and practices that make research FAIR (Findable, Accessible, Interoperable, Reusable); researchers are making their data and preliminary publications openly accessible, and then publishers are making the peer-reviewed research immediately and freely available to all. The rapid dissemination of research—through preprints in particular as well as journal articles—stands in contrast to what happened in the 2003 SARS outbreak when the majority of research on the disease was published well after the outbreak had ended.

Many outside observers might reasonably assume, given the digital world we all now inhabit, that science usually works like this. Yet this is very far from the norm for most research. Science is not something that just happens in response to emergencies or specific events—it is an ongoing, largely publicly funded, national and international enterprise….

Sharing of the underlying data that journal articles are based on is not yet a universal requirement for publication, nor are researchers usually recognised for data sharing.

There are many benefits associated with an open science model. Image adapted from: Gaelen Pinnock/UCT; CC-BY-SA 4.0 .

Once published, even access to research is not seamless. The majority of academic journals still require a subscription to access. Subscriptions are expensive; Australian universities alone currently spend more than $300 million per year on subscriptions to academic journals. Access to academic journals also varies between universities with varying library budgets. The main markets for subscriptions to the commercial journal literature are higher education and health, with some access to government and commercial….(More)”.

Smart cities during COVID-19: How cities are turning to collective intelligence to enable smarter approaches to COVID-19.

Curated on Posted on by Stefaan Verhulst

Article by Peter Baeck and Sophie Reynolds: One of the most prominent examples of how technology and data is being used to empower citizens is happening in Seoul. Here the city has used its ‘citizens as mayors’ philosophy for smart cities; an approach which aims to equip citizens with the same real-time access to information as the mayor. Seoul has gone further than most cities in making information about the COVID-19 outbreak in the city accessible to citizens. Its dashboard is updated multiple times daily and allows citizens to access the latest anonymised information on confirmed patients’ age, gender and dates of where they visited and when, after developing symptoms. Citizens can access even more detailed information; down to visited restaurants and cinema seat numbers.

The goal is to provide citizens with the information needed to take precautionary measures, self-monitor and report if they start showing symptoms after visiting one of the “infection points.” To help allay people’s fears and reduce the stigma associated with businesses that have been identified as “infection points”, the city government also provides citizens with information about the nearest testing clinics and makes “clean zones” (places that have been disinfected after visits by confirmed patients) searchable for users.

In addition to national and institutional responses there are (at least) five ways collective intelligence approaches are helping city governments, companies and urban communities in the fight against COVID-19:

1. Open sharing with citizens about the spread and management of COVID-19:

Based on open data provided by public agencies, private sector companies are using the city as a platform to develop their own real-time dashboards and mobile apps to further increase public awareness and effectively disseminate disease information. This has been the case with Corona NOW, Corona Map, Corona 100m in Seoul, Korea – which allow people to visualise data on confirmed coronavirus patients, along with patients’ nationality, gender, age, which places the patient has visited, and how close citizens are to these coronavirus patients. Developer Lee Jun-young who created the Corona Map app, said he built it because he found that the official government data was too difficult to understand.

Meanwhile in city state Singapore, the dashboard developed by UpCode scrapes data provided by the Singapore Ministry of Health’s own dashboard (which is exceptionally transparent about coronavirus case data) to make it cleaner and easier to navigate, and vastly more insightful. For instance, it allows you to learn about the average recovery time for those infected.

UpCode is making its platform available for others to re-use in other contexts.

2. Mobilising community-led responses to tackle COVID-19

Crowdfunding is being used in a variety of ways to get short-term targeted funding to a range of worthy causes opened up by the COVID-19 crisis. Examples include helping to fundraise for community activities for those directly affected by the crisis, backing tools and products that can address the crisis (such as buying PPE) and pre-purchasing products and services from local shops and artists. A significant proportion of the UK’s 1,000 plus mutual aid initiatives are now turning to crowdfunding as a way to rapidly respond to the new and emerging needs occurring at the city-wide and hyperlocal (i.e. streets and neighbourhood) levels.

Aberdeen City Mutual Aid group set up a crowdfunded community fund to cover the costs of creating a network of volunteers across the city, as well as any expenses incurred at food shops, fuel costs for deliveries and purchasing other necessary supplies. Similarly, the Feed the Heroes campaign was launched with an initial goal of raising €250 to pay for food deliveries for frontline staff who are putting in extra hours at the Mater Hospital, Dublin during the coronavirus outbreak….(More)”.

Covid-19 means systems thinking is no longer optional

Curated on Posted on by Stefaan Verhulst

Seth Reynolds at NPC: “Never has the interdependence of our world been experienced by so many, so directly, so rapidly and so simultaneously. Our response to one threat, Covid-19, has unleashed a deluge of secondary and tertiary consequences that have swept across the globe faster than the virus itself. The butterfly effect has taken on new dimensions, as the reality of system interdependence at multiple levels has been brought directly into our homes and news feeds:

  • Individually, an innocuous bus journey sends a stranger to intensive care in a fortnight
  • Societally, health charities are warning that actions taken in response to one health crisis – Covid-19 – could lead to up to 11,000 deaths of women in childbirth around the world because of another – namely, 9.5m women not getting access to family planning intervention.
  • Governmentally, some systemic consequences of decision-making are there for all to see, while others are less immediately apparent – for example, Trump’s false proclamation of testing availability “for anyone that wants one”  ended up actually reducing the availability of tests by immediately increasing demand.  It even reduced the already scarce supply of protective masks, which must be disposed of after testing.

Students will be studying coronavirus for years. A systems lens can help us learn essential lessons. Covid-19 has provided many clear examples of effective systemic action, and stark lessons in the consequences of non-systemic thinking. Leaders and decision-makers everywhere are being compelled to think broader and deeper about causation and consequence. Decisions taken, even words spoken, without systemic awareness can have – indeed have had – profoundly damaging effects.

Systemic thinking, planning, action and leadership must now be mainstreamed – individually, organisationally, societally, across public, private and charity sectors. As one American diplomat recently reflected: “from climate change to the coronavirus, complex adaptive systems thinking is key to handling crises”. In fact, some epidemiologists, suddenly the world’s most valuable profession, have been calling for more systemic ways of working for years. However, we currently do not think and act in accordance with how our complex systems function and this has been part of the Covid-19 problem…(More)”.

How Statistics Can Help — Going Beyond COVID-19

Curated on Posted on by Stefaan Verhulst

Blog by Walter J. Radermacher at Data & Policy: “It is rightly pointed out that in the midst of a crisis of enormous dimensions we needed high quality statistics with utmost urgency, but that instead we are in danger of drowning in an ocean of data and information. The pandemic is accompanied and exacerbated by an infodemic. At this moment, and in this confusion and search for solutions, it seems appropriate to take advice from previous initiatives and draw lessons for the current situation. More than 20 years ago in the United Kingdom, the report “Statistics — A Matter of Trust” laid the foundations for overcoming the previously spreading crisis of confidence through a solidly structured statistical system. This report does not stand alone in international comparison. Rather, it is one of a series of global, European and national measures and agreements which, since the fall of the Berlin Wall in 1989, have strengthened official statistics as the backbone of policy in democratic societies, with the UN Fundamental Statistical Principles and the EU Statistics Code of Practice being prominent representatives. So, if we want to deal with our current difficulties, we should address precisely those points that have emerged as determining factors for the quality of statistics, with the following three questions: What (statistical products, quality profile)? How (methods)? Who (institutions)? The aim must be to ensure that statistical information is suitable for facilitating the resolution of conflicts by eliminating the need to argue about the facts and only about the conclusions to be drawn from them.

In the past, this task would have led relatively quickly to a situation where the need for information would have been directed to official statistics as the preferred provider; this has changed recently for many reasons. On the one hand, there is the danger that the much-cited data revolution and learning algorithms (so-called AI) are presented as an alternative to official statistics (which are perceived as too slow, too inflexible and too expensive), instead of emphasizing possible commonalities and cross-fertilization possibilities. On the other hand, after decades of austerity policies, official statistics are in a similarly defensive situation to that of the public health system in many respects and in many countries: There is a lack of financial reserves, personnel and know-how for the new and innovative work now so urgently needed.

It is therefore required, as in the 1990s, to ask the fundamental question again, namely, do we (still and again) really deserve official statistics as the backbone of democratic decision-making, and if so, what should their tasks be, how should they be financed and anchored in the political system?…(More)”.

The public debate around COVID-19 demonstrates our ongoing and misplaced trust in numbers

Curated on Posted on by Stefaan Verhulst

Ville Aula at LSE Blogs: “Read the front page of any major newspaper and I guarantee that the latest number of patients who have tested positive for COVID-19 and the number of mortalities will feature heavily. Open your social media accounts and you will quickly encounter graphs that show the mounting numbers of cases in different countries, complemented by modelling projections. These numbers and graphs feed the popular imagination of how well countries are “flattening the curve”, a concept that has brought epidemiological modelling inspired language to everyone’s lips. 

Numbers, graphs, and data are thus playing an essential part in how we experience the pandemic. The endless flows of numbers from different countries are meticulously compared with those from others. These comparisons then form the basis to how individual countries are portrayed and ranked in the global pandemic drama. 

But, there is also doubt in the air. We distrust the existing numbers and call for ever-more accurate information. For example, there has been a lively debate on how widespread the pandemic has been in China, an issue that connects directly to how tests are administered and cases reported. Equally, numbers from Europe do not provide indisputable or uniform information on the pandemic either, because their collection is subject to vastly different policies, practices, and contexts that make comparisons difficult. We also lack the scientific consensus that would allow us to link the number of mortalities to the prevalence of the virus, yet mortalities are still often taken as the most solid form of information on the pandemic.  These doubts have fuelled demands to do systematic population level testing of the virus prevalence, which is just a different way of saying that we need more numbers. 

Numbers are thus both the problem and the solution, and we want more of them. However, what makes numbers useful for developing better treatments and policies, does not necessarily lead to the same outcomes when applied to public debate. In the broader sphere of public debate, such tendencies reveal a longing for the veracity of data during times of uncertainty. Even when such calls are founded on demands for transparency in the name of democracy or healthy skepticism of existing data, they are entangled in a faulty logic of data itself eventually providing a solid standing for public debate….(More)”.

Viruses Cross Borders. To Fight Them, Countries Must Let Medical Data Flow, Too

Curated on Posted on by Stefaan Verhulst

Nigel Cory at ITIF: “If nations could regulate viruses the way many regulate data, there would be no global pandemics. But the sad reality is that, in the midst of the worst global pandemic in living memory, many nations make it unnecessarily complicated and costly, if not illegal, for health data to cross their borders. In so doing, they are hindering critically needed medical progress.

In the COVID-19 crisis, data analytics powered by artificial intelligence (AI) is critical to identifying the exact nature of the pandemic and developing effective treatments. The technology can produce powerful insights and innovations, but only if researchers can aggregate and analyze data from populations around the globe. And that requires data to move across borders as part of international research efforts by private firms, universities, and other research institutions. Yet, some countries, most notably China, are stopping health and genomic data at their borders.

Indeed, despite the significant benefits to companies, citizens, and economies that arise from the ability to easily share data across borders, dozens of countries—across every stage of development—have erected barriers to cross-border data flows. These data-residency requirements strictly confine data within a country’s borders, a concept known as “data localization,” and many countries have especially strict requirements for health data.

China is a noteworthy offender, having created a new digital iron curtain that requires data localization for a range of data types, including health data, as part of its so-called “cyber sovereignty” strategy. A May 2019 State Council regulation required genomic data to be stored and processed locally by Chinese firms—and foreign organizations are prohibited. This is in service of China’s mercantilist strategy to advance its domestic life sciences industry. While there has been collaboration between U.S. and Chinese medical researchers on COVID-19, including on clinical trials for potential treatments, these restrictions mean that it won’t involve the transfer, aggregation, and analysis of Chinese personal data, which otherwise might help find a treatment or vaccine. If China truly wanted to make amends for blocking critical information during the early stages of the outbreak in Wuhan, then it should abolish this restriction and allow genomic and other health data to cross its borders.

But China is not alone in limiting data flows. Russia requires all personal data, health-related or not, to be stored locally. India’s draft data protection bill permits the government to classify any sensitive personal data as critical personal data and mandate that it be stored and processed only within the country. This would be consistent with recent debates and decisions to require localization for payments data and other types of data. And despite its leading role in pushing for the free flow of data as part of new digital trade agreements, Australia requires genomic and other data attached to personal electronic health records to be only stored and processed within its borders.

Countries also enact de facto barriers to health and genomic data transfers by making it harder and more expensive, if not impractical, for firms to transfer it overseas than to store it locally. For example, South Korea and Turkey require firms to get explicit consent from people to transfer sensitive data like genomic data overseas. Doing this for hundreds or thousands of people adds considerable costs and complexity.

And the European Union’s General Data Protection Regulation encourages data localization as firms feel pressured to store and process personal data within the EU given the restrictions it places on data transfers to many countries. This is in addition to the renewed push for local data storage and processing under the EU’s new data strategy.

Countries rationalize these steps on the basis that health data, particularly genomic data, is sensitive. But requiring health data to be stored locally does little to increase privacy or data security. The confidentiality of data does not depend on which country the information is stored in, only on the measures used to store it securely, such as via encryption, and the policies and procedures the firms follow in storing or analyzing the data. For example, if a nation has limits on the use of genomics data, then domestic organizations using that data face the same restrictions, whether they store the data in the country or outside of it. And if they share the data with other organizations, they must require those organizations, regardless of where they are located, to abide by the home government’s rules.

As such, policymakers need to stop treating health data differently when it comes to cross-border movement, and instead build technical, legal, and ethical protections into both domestic and international data-governance mechanisms, which together allow the responsible sharing and transfer of health and genomic data.

This is clearly possible—and needed. In February 2020, leading health researchers called for an international code of conduct for genomic data following the end of their first-of-its-kind international data-driven research project. The project used a purpose-built cloud service that stored 800 terabytes of genomic data on 2,658 cancer genomes across 13 data centers on three continents. The collaboration and use of cloud computing were transformational in enabling large-scale genomic analysis….(More)”.