Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Curated on Posted on by Stefaan Verhulst

Paper by Mirco Nanni: “The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking.

We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity.

This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates – if and when they want, for specific aims – with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society….(More)”

We Have the Power to Destroy Ourselves Without the Wisdom to Ensure That We Don’t

Curated on Posted on by Stefaan Verhulst

EdgeCast by Toby Ord: “Lately, I’ve been asking myself questions about the future of humanity, not just about the next five years or even the next hundred years, but about everything humanity might be able to achieve in the time to come.

The past of humanity is about 200,000 years. That’s how long Homo sapiens have been around according to our current best guess (it might be a little bit longer). Maybe we should even include some of our other hominid ancestors and think about humanity somewhat more broadly. If we play our cards right, we could live hundreds of thousands of years more. In fact, there’s not much stopping us living millions of years. The typical species lives about a million years. Our 200,000 years so far would put us about in our adolescence, just old enough to be getting ourselves in trouble, but not wise enough to have thought through how we should act.

But a million years isn’t an upper bound for how long we could live. The horseshoe crab, for example, has lived for 450 million years so far. The Earth should remain habitable for at least that long. So, if we can survive as long as the horseshoe crab, we could have a future stretching millions of centuries from now. That’s millions of centuries of human progress, human achievement, and human flourishing. And if we could learn over that time how to reach out a little bit further into the cosmos to get to the planets around other stars, then we could have longer yet. If we went seven light-years at a time just making jumps of that distance, we could reach almost every star in the galaxy by continually spreading out from the new location. There are already plans in progress to send spacecraft these types of distances. If we could do that, the whole galaxy would open up to us….

Humanity is not a typical species. One of the things that most worries me is the way in which our technology might put us at risk. If we look back at the history of humanity these 2000 centuries, we see this initially gradual accumulation of knowledge and power. If you think back to the earliest humans, they weren’t that remarkable compared to the other species around them. An individual human is not that remarkable on the Savanna compared to a cheetah, or lion, or gazelle, but what set us apart was our ability to work together, to cooperate with other humans to form something greater than ourselves. It was teamwork, the ability to work together with those of us in the same tribe that let us expand to dozens of humans working together in cooperation. But much more important than that was our ability to cooperate across time, across the generations. By making small innovations and passing them on to our children, we were able to set a chain in motion wherein generations of people worked across time, slowly building up these innovations and technologies and accumulating power….(More)”.

Tracking coronavirus: big data and the challenge to privacy

Curated on Posted on by Stefaan Verhulst

Nic Fildes and Javier Espinoza at the Financial Times: “When the World Health Organization launched a 2007 initiative to eliminate malaria on Zanzibar, it turned to an unusual source to track the spread of the disease between the island and mainland Africa: mobile phones sold by Tanzania’s telecoms groups including Vodafone, the UK mobile operator.

Working together with researchers at Southampton university, Vodafone began compiling sets of location data from mobile phones in the areas where cases of the disease had been recorded. 

Mapping how populations move between locations has proved invaluable in tracking and responding to epidemics. The Zanzibar project has been replicated by academics across the continent to monitor other deadly diseases, including Ebola in west Africa….

With much of Europe at a standstill as a result of the coronavirus pandemic, politicians want the telecoms operators to provide similar data from smartphones. Thierry Breton, the former chief executive of France Telecom who is now the European commissioner for the internal market, has called on operators to hand over aggregated location data to track how the virus is spreading and to identify spots where help is most needed.

Both politicians and the industry insist that the data sets will be “anonymised”, meaning that customers’ individual identities will be scrubbed out. Mr Breton told the Financial Times: “In no way are we going to track individuals. That’s absolutely not the case. We are talking about fully anonymised, aggregated data to anticipate the development of the pandemic.”

But the use of such data to track the virus has triggered fears of growing surveillance, including questions about how the data might be used once the crisis is over and whether such data sets are ever truly anonymous….(More)”.

Online collective intelligence course aims to improve responses to COVID-19 and other crises

Curated on Posted on by Stefaan Verhulst

PressRelease: “Working with 11 partner institutions around the world,  The Governance Lab (The GovLab) at the New York University Tandon School of Engineering today launches a massive open online course (MOOC) on “Collective Crisis Intelligence.” The course is free, open to anyone, and designed to help institutions improve disaster response through the use of data and volunteer participation. 

Thirteen modules have been created by leading global experts in major disasters such as the post-election violence in Kenya in 2008, the Fukushima nuclear plant disaster in 2011, the Ebola crisis in 2014, the Zika outbreak in 2016, and the current coronavirus. The course is designed to help those responding to coronavirus make use of volunteerism. 

As the COVID-19 pandemic reaches unprecedented proportions and spreads to more than 150 countries on six continents, policymakers are struggling to answer questions such as “How do we predict how the virus will spread?,” “How do we help the elderly and the homebound?,” “How do we provide economic assistance to those affected by business closures?,” and more. 

In each mini-lecture, those who have learned how to mobilize groups of people online to manage in a crisis present the basic concepts and tools to learn, analyze, and implement a crowdsourced public response. Lectures include

  • Introduction: Why Collective Intelligence Matters in a Crisis
  • Defining Actionable Problems (led by Matt Andrews, Harvard Kennedy School)
  • Three Day Evidence Review (led by Peter Bragge, Monash University, Australia)
  • Priorities for Collective Intelligence (led by Geoff Mulgan, University College London) 
  • Smarter Crowdsourcing (led by Beth Simone Noveck, The GovLab)
  • Crowdfunding (led by Peter Baeck, Nesta, United Kingdom)
  • Secondary Fall Out (led by Azby Brown, Safecast, Japan)
  • Crowdsourcing Surveillance (led by Tolbert Nyenswah, Johns Hopkins Bloomberg School of Public Health, United States/Liberia)
  • Crowdsourcing Data (led by Angela Oduor Lungati and Juliana Rotich, Ushahidi, Kenya)
  • Mobilizing a Network (led by Sean Bonner, Safecast, Japan)
  • Crowdsourcing Scientific Expertise (led by Ali Nouri, Federation of American Scientists)
  • Chatbots and Social Media Strategies for Crisis (led by Nashin Mahtani, PetaBencana.id, Indonesia)
  • Conclusion: Lessons Learned

The course explores such innovative uses of crowdsourcing as Safecast’s implementation of citizen science to gather information about environmental conditions after the meltdown of the Fukushima nuclear plant; Ushahidi, an online platform in Kenya for crowdsourcing data for crisis relief, human rights advocacy, transparency, and accountability campaigns; and “Ask a Scientist,” an interactive tool developed by The GovLab with the Federation of American Scientists and the New Jersey Office of Innovation, in which a network of scientists answer citizens’ questions about COVID-19.

More information on the courses is available at https://covidcourse.thegovlab.org “

New Tool to Establish Responsible Data Collaboratives in the Time of COVID-19

Curated on Posted on by Stefaan Verhulst

Announcement: “To address the COVID-19 pandemic and other dynamic threats, The GovLab has called for the development of a new data infrastructure and ecosystem. Establishing data collaboratives in a responsible manner often necessitates the creation of data sharing agreements and other legal documentation — a strain on time and capacity both for data holders and those who could use data in the public interest.

Today, to support the development of data collaboratives in a responsible and agile way, we are sharing a new tool that addresses the complexity in preparing a Data Sharing Agreement from Contracts for Data Collaboration (a joint initiative of SDSN-TReNDS, the World Economic Forum, The GovLab, and the University of Washington’s Information Risk Research Initiative). Providing a checklist to support organizations with reviewing, negotiating and preparing Data Sharing Arrangements, the intent is to strengthen stakeholder trust and help accelerate responsible data sharing arrangements given the urgency of the global pandemic.

(Please note that the check list is a tool for formulating and understanding legal issues, but we are not offering it as legal advice.)

CLICK HERE TO DOWNLOAD THE TOOL (More)”.

The Responsible Data for Children (RD4C) Case Studies

Curated on Posted on by Stefaan Verhulst

Andrew Young at Datastewards.net: “This week, as part of the Responsible Data for Children initiative (RD4C), the GovLab and UNICEF launched a new case study series to provide insights on promising practice as well as barriers to realizing responsible data for children.

Drawing upon field-based research and established good practice, RD4C aims to highlight and support responsible handling of data for and about children; identify challenges and develop practical tools to assist practitioners in evaluating and addressing them; and encourage a broader discussion on actionable principles, insights, and approaches for responsible data management.

RD4C launched in October 2019 with the release of the RD4C Synthesis Report, Selected Readings, and the RD4C Principles: Purpose-Driven, People-Centric, Participatory, Protective of Children’s Rights, Proportional, Professionally Accountable, and Prevention of Harms Across the Data Lifecycle.

The RD4C Case Studies analyze data systems deployed in diverse country environments, with a focus on their alignment with the RD4C Principles. This week’s release includes case studies arising from field missions to Romania, Kenya, and Afghanistan in 2019. The data systems examined are:

Combating COVID-19 with Data: What Role for National Statistical Systems?

Curated on Posted on by Stefaan Verhulst

Press Release: “As part of its ongoing response to the COVID-19 crisis, PARIS21 released today a policy brief at the intersection of statistics and policy making to help inform the measures taken to address the pandemic.

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The COVID-19 pandemic has brought data to the centre of policy making and public attention. A diverse ecosystem of data producers, both private and public, report rates of infection, fatality and recovery on a daily basis. However, a proliferation of data, which is at times contradictory, can also lead to confusion and mistrust among data users.

Meanwhile, policymakers, development partners and citizens need to take quick, informed actions to design interventions that reach the most vulnerable and leave no one behind. As countries comply with lockdowns and other containment measures, national statistical systems (NSSs) face a dual effect of growing data demand and constrained supply. This in turn may squeeze NSSs beyond their institutional capacity.

At the same time, alternative data sources such as mobile phone or satellite data are in abundance. These data could potentially complement traditional sources such as censuses, surveys and administrative systems. However, with scant governance frameworks to scale and sustain their use, policy action is not yet based on a convergence of evidence.

This policy brief introduces a conceptual framework that describes the adverse effects of the crisis on NSSs in developing countries. Moreover, it suggests short and medium-term actions to mitigate the negative effects by:

1. Focusing data production on priority economic, social and demographic data.
2. Communicating proactively with citizens, academia, private sector and policy makers.
3. Positioning the NSO as advisor and knowledge bank for national governments.

NSSs contribute significantly to robust policy responses in a crisis. The brief thus calls on national statistical offices to assume a central role as coordinators of the NSSs and chart the way toward improved data ecosystem governance for informing policies during and after COVID-19….(More)”.

A widening data divide: COVID-19 and the Global South

Curated on Posted on by Stefaan Verhulst

Essay by stefania milan and Emiliano Treré at Data & Policy: “If numbers are the conditions of existence of the COVID-19 problem, we ought to pay attention to the actual (in)ability of many countries in the South to test their population for the virus, and to produce reliable population statistics more in general — let alone to adequately care for them. It is a matter of a “data gap” as well as of data quality, which even in “normal” times hinders the need for “evidence-based policy making, tracking progress and development, and increasing government accountability” (Chen et al., 2013). And while the World Health Organization issues warning about the “dramatic situation” concerning the spread of COVID-19 in the African continent, to name just one of the blind spots of our datasets of the global pandemic, the World Economic Forum calls for “flattening the curve” in developing countries. Progress has been made following the revision of the United Nations’ Millennium Development Goals in 2005, with countries in the Global South have been invited (and supported) to devise National Strategies for the Development of Statistics. Yet, a cursory look at the NYU GovLab’s valuable repository of data collaboratives” addressing the COVID-19 pandemic reveals the virtual absence of data collection and monitoring projects in the South of the hemisphere. The next obvious step is the dangerous equation “no data=no problem”.

Disease and “whiteness”

Epidemiology and pharmacogenetics (i.e. the study of the genetic basis of how people respond to pharmaceuticals), to name but a few amongst the number of concerned life sciences, are largely based on the “inclusion of white/Caucasians in studies and the exclusion of other ethnic groups” (Tutton, 2007). In other words, modeling of disease evolution and the related solutions are based on datasets that take into account primarily — and in fact almost exclusively — the caucasian population. This is a known problem in the field, which derives from the “assumption that a Black person could be thought of as being White”, dismissing specificities and differences. This problem has been linked to the “lack of social theory development, due mainly to the reluctance of epidemiologists to think about social mechanisms (e.g., racial exploitation)” (Muntaner, 1999, p. 121). While COVID-19 represents a slight variation on this trend, having been first identified in China, the problem on the large scale remains. And in times of a health emergency as global as this one, risks to be reinforced and perpetuated.

A succulent market for the industry

In the lack of national testing capacity, the developing world might fall prey to the blooming industry of genetic and disease testing, on the one hand, and of telecom-enabled population monitoring on the other. Private companies might be able to fill the gap left by the state, mapping populations at risk — while however monetizing their data. The case of 23andme is symptomatic of this rise of industry-led testing, which constitutes a double-edge sword. On the one hand, private actors might supply key services that resource-poor or failing states are unable to provide. On the other hand, however, the distorted and often hidden agendas of profit-led players reveals its shortcomings and dangers. If we look at the telecom industry, we note how it has contributed to track disease propagation in a number of health emergencies such as Ebola. And if the global open data community has called for smoother data exchange between the private and the public sector to collectively address the spread of the virus,in the absence of adequate regulatory frameworks in the Global South, for example in the field of privacy and data retention, local authorities might fall prey to outside interventions of dubious nature….(More)”.

Open Covid Pledge

Curated on Posted on by Stefaan Verhulst

Pledge: “Immediate action is required to halt the COVID-19 Pandemic and treat those it has affected. It is a practical and moral imperative that every tool we have at our disposal be applied to develop and deploy technologies on a massive scale without impediment.

We therefore pledge to make our intellectual property available free of charge for use in ending the COVID-19 pandemic and minimizing the impact of the disease.

We will implement this pledge through a license that details the terms and conditions under which our intellectual property is made available.

How to make the Pledge

The first step for organizations wishing to make the Pledge is to publicly commit to making intellectual property relevant to COVID-19 freely available, by:

  • Posting a public statement that the organization is making the Pledge, on their website. 
  • Issuing an official press release.

And then sending us a link to this statement, a point of contact in the organization, and, at the organization’s discretion, a copy of their logo to display on this site.

How to implement the Pledge

The next step for organizations who have made the Pledge is to implement it via a license detailing the terms and conditions under which their intellectual property is made available. There are three options for doing so:

  • Adopt the Open COVID License, created by our legal team for organizations that wish to implement the Pledge simply and immediately on terms shared by many other organizations.
  • Create a custom license that accomplished the intent of the Pledge.
  • Identify existing license(s) that accomplish the goals of the Pledge.

As with making the Pledge, send us links to the license or licenses, a point of contact in the organization, and, at the organization’s discretion, a copy of their logo to display on this site….(More)”.

Coronavirus: country comparisons are pointless unless we account for these biases in testing

Curated on Posted on by Stefaan Verhulst

Norman Fenton, Magda Osman, Martin Neil, and Scott McLachlan at The Conversation: “Suppose we wanted to estimate how many car owners there are in the UK and how many of those own a Ford Fiesta, but we only have data on those people who visited Ford car showrooms in the last year. If 10% of the showroom visitors owned a Fiesta, then, because of the bias in the sample, this would certainly overestimate the proportion of Ford Fiesta owners in the country.

Estimating death rates for people with COVID-19 is currently undertaken largely along the same lines. In the UK, for example, almost all testing of COVID-19 is performed on people already hospitalised with COVID-19 symptoms. At the time of writing, there are 29,474 confirmed COVID-19 cases (analogous to car owners visiting a showroom) of whom 2,352 have died (Ford Fiesta owners who visited a showroom). But it misses out all the people with mild or no symptoms.

Concluding that the death rate from COVID-19 is on average 8% (2,352 out of 29,474) ignores the many people with COVID-19 who are not hospitalised and have not died (analogous to car owners who did not visit a Ford showroom and who do not own a Ford Fiesta). It is therefore equivalent to making the mistake of concluding that 10% of all car owners own a Fiesta.

There are many prominent examples of this sort of conclusion. The Oxford COVID-19 Evidence Service have undertaken a thorough statistical analysis. They acknowledge potential selection bias, and add confidence intervals showing how big the error may be for the (potentially highly misleading) proportion of deaths among confirmed COVID-19 patients.

They note various factors that can result in wide national differences – for example the UK’s 8% (mean) “death rate” is very high compared to Germany’s 0.74%. These factors include different demographics, for example the number of elderly in a population, as well as how deaths are reported. For example, in some countries everybody who dies after having been diagnosed with COVID-19 is recorded as a COVID-19 death, even if the disease was not the actual cause, while other people may die from the virus without actually having been diagnosed with COVID-19.

However, the models fail to incorporate explicit causal explanations in their modelling that might enable us to make more meaningful inferences from the available data, including data on virus testing.

What a causal model would look like. Author provided

We have developed an initial prototype “causal model” whose structure is shown in the figure above. The links between the named variables in a model like this show how they are dependent on each other. These links, along with other unknown variables, are captured as probabilities. As data are entered for specific, known variables, all of the unknown variable probabilities are updated using a method called Bayesian inference. The model shows that the COVID-19 death rate is as much a function of sampling methods, testing and reporting, as it is determined by the underlying rate of infection in a vulnerable population….(More)”