Commit to transparent COVID data until the WHO declares the pandemic is over

Curated on Posted on by Stefaan Verhulst

Edouard Mathieu at Nature: “…There are huge inequalities in data reporting around the world. Most of my time over the past two years has been spent digging through official websites and social-media accounts of hundreds of governments and health authorities. Some governments still report official statistics in low-resolution images on Facebook or infrequent press conferences on YouTube — often because they lack resources to do better. Some countries, including China and Iran, have provided no files at all.

Sometimes, it’s a lack of awareness: government officials might think that a topline figure somewhere in a press release is sufficient. Sometimes, the problem is reluctance: publishing the first file would mean a flood of requests for more data that authorities can’t or won’t publish.

Some governments rushed to launch pandemic dashboards, often built as one-off jobs by hired contractors. Civil servants couldn’t upgrade them as the pandemic shifted and new metrics and charts became more relevant. I started building our global data set on COVID-19 vaccinations in 2021, but many governments didn’t supply data for weeks — sometimes months — after roll-outs because their dashboards couldn’t accommodate the data. Worse, they rarely supplied underlying data essential for others to download and produce their own analyses. (My team asked repeatedly.)

Over and over, I’ve seen governments emphasize making dashboards look good when the priority should be making data available. A simple text file would do. After all, research groups like mine and citizens with expertise in data-visualization tools are more than willing to create a useful website or mobile app. But to do so, we need the raw material in a machine-readable format….(More)”.

End the State Monopoly on Facts

Curated on Posted on by Stefaan Verhulst

Essay by Adam J. White: “…This Covid-era dynamic has accelerated broader trends toward the consolidation of informational power among a few centralized authorities. And it has further deformed the loose set of institutions and norms that Jonathan Rauch, in a 2018 National Affairs article, identified as Western civilization’s “constitution of knowledge.” This is an arrangement in science, journalism, and the courts in which “any hypothesis can be floated” but “can join reality only insofar as it persuades people after withstanding vigorous questioning and criticism.” The more that Americans delegate the hard work of developing and distributing information to a small number of regulatory institutions, the less capable we all will be of correcting the system’s mistakes — and the more likely the system will be to make mistakes in the first place.

In a 1999 law review article, Timur Kuran and Cass Sunstein warned of availability cascades, a process in which activists promote factual assertions and narratives that in a self-reinforcing dynamic become more plausible the more widely available they are, and can eventually overwhelm the public’s perception. The Covid-19 era has been marked by the opposite problem: unavailability cascades, in which media institutions and social media platforms swiftly erase disfavored narratives and dissenting contentions from the marketplace of ideas, making them seem implausible by their near unavailability. Such cascades occur because legacy media and social media platforms have come to rely overwhelmingly, even exclusively, on federal regulatory agencies’ factual assertions and the pronouncements of a small handful of other favored institutions, such as the World Health Organization, as the gold standard of facts. But availability and unavailability cascades, even when intended in good faith to prevent the spread of disinformation among the public, risk misinforming the very people they purport to inform. A more diverse and vibrant ecosystem of informational institutions would disincentivize the platforms’ and media’s reflexive, cascading reactions to dissenting views.

This second problem — the concentration of informational power — exacerbates the first one: how to counterbalance the executive branch’s power after an emergency. In order for Congress, the courts, and other governing institutions to reassert their own constitutional roles after the initial weeks and months of crisis, they (and the public) need credible sources of information outside the administration itself. An informational ecosystem not overweighted so heavily toward administrative agencies, one that benefits more from the independent contributions of experts in universities, think tanks, journalism, and other public and private institutions, would improve the quality of information that it produces. It would also be less susceptible to the reflexively partisan skepticism that has become endemic in the polarization of modern president-centric government…(More)”.

Data saves lives: reshaping health and social care with data

Curated on Posted on by Stefaan Verhulst

UK Department of Health and Social Care: “In England and in every community around the world, digital developments have been essential to the pandemic response. People have accessed advice and care remotely in unprecedented numbers, helping keep them and their families safe. World class genomics helped identify and track new variants. Daily analysis allowed problems to be understood rapidly, and resources redeployed. Staff worked remotely. And the COVID-19 vaccination service was mobilised in record time.

Such an efficient and effective response was only possible because of investment in digital systems, innovation and skills over the last few years, and the partnerships forged between digital, clinical and operational colleagues.

The opportunity now is for the health and care sector to apply such approaches with increased urgency and consistency to both our long-term challenges and to the immediate tasks of rebuilding from the pandemic. We have a responsibility to do both.

The Digital Transformation Plan sets out the overarching vision for how we will digitise, connect and transform the health and care sector. This data strategy explains in more detail the role that data will play in that transformation and how it can inspire effective collaboration across the NHS, adult social care, and public health, help us care for people in the best possible way, and ensuring that our citizens have the best experience possible when using the system.

There are 3 key priorities which underpin this strategy:

  • first to build understanding on how data is used and the potential for data-driven innovation, improving transparency so the public has control over how we are using their data
  • second to make appropriate data sharing the norm and not the exception across health, adult social care and public health, to provide the best care possible to the citizens we serve, and to support staff throughout the health and care system
  • third to build the right foundations – technical, legal, regulatory – to make that possible…(More)”.

A 680,000-person megastudy of nudges to encourage vaccination in pharmacies

Curated on Posted on by Stefaan Verhulst

Paper by Katherine L. Milkman et al: “Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most-effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top-performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was “waiting for you.” Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy….(More)”.

How NFTs could transform health information exchange

Curated on Posted on by Stefaan Verhulst

Paper by Kristin Kostick-Quenet et al: “Personal (sometimes called “protected”) health information (PHI) is highly valued and will become centrally important as big data and machine learning move to the forefront of health care and translational research. The current health information exchange (HIE) market is dominated by commercial and (to a lesser extent) not-for-profit entities and typically excludes patients. This can serve to undermine trust and create incentives for sharing data. Patients have limited agency in deciding which of their data is shared, with whom, and under what conditions. Within this context, new forms of digital ownership can inspire a digital marketplace for patient-controlled health data. We argue that nonfungible tokens (NFTs) or NFT-like frameworks can help incentivize a more democratized, transparent, and efficient system for HIE in which patients participate in decisions about how and with whom their PHI is shared…(More)”.

COVID’s lesson for governments? Don’t cherry-pick advice, synthesize it

Curated on Posted on by Stefaan Verhulst

Essay by Geoff Mulgan: “Too many national leaders get good guidance yet make poor decisions…Handling complex scientific issues in government is never easy — especially during a crisis, when uncertainty is high, stakes are huge and information is changing fast. But for some of the nations that have fared the worst in the COVID-19 pandemic, there’s a striking imbalance between the scientific advice available and the capacity to make sense of it. Some advice is ignored because it’s politically infeasible or unpragmatic. Nonetheless, much good scientific input has fallen aside because there’s no means to pick it up.

Part of the problem has been a failure of synthesis — the ability to combine insights and transcend disciplinary boundaries. Creating better syntheses should be a governmental priority as the crisis moves into a new phase….

Input from evidence synthesis is crucial for policymaking. But the capacity of governments to absorb such evidence is limited, and syntheses for decisions must go much further in terms of transparently incorporating assessments of political or practical feasibility, implementation, benefits and cost, among many other factors. The gap between input and absorption is glaring.

I’ve addressed teams in the UK prime minister’s office, the European Commission and the German Chancellery about this issue. In responding to the pandemic, some countries (including France and the United Kingdom) have tried to look at epidemiological models alongside economic ones, but none has modelled the social or psychological effects of different policy choices, and none would claim to have achieved a truly synthetic approach.

There are dozens of good examples of holistic thinking and action: programmes to improve public health in Finland, cut UK street homelessness, reduce poverty in China. But for many governments, the capacity to see things in the round has waned over the past decade. The financial crisis of 2007 and then populism both shortened governments’ time horizons for planning and policy in the United States and Europe….

The worst governments rely on intuition. But even the best resort to simple heuristics — for example, that it’s best to act fast, or that prioritizing health is also good for the economy. This was certainly true in 2020 and 2021. But that might change with higher vaccination and immunity rates.

What would it mean to transcend simple heuristics and achieve a truly synthetic approach? It would involve mapping and ranking relevant factors (from potential impacts on hospital capacity to the long-run effects of isolation); using formal and informal models to capture feedbacks, trade-offs and synergies; and more creative work to shape options.

Usually, such work is best done by teams that encompass breadth and depth, disparate disciplines, diverse perspectives and both officials and outsiders. Good examples include Singapore’s Strategy Group (and Centre for Strategic Futures), which helps the country to execute sophisticated plans on anything from cybercrime to climate resilience. But most big countries, despite having large bureaucracies, lack comparable teams…(More)”.

Suicide hotline shares data with for-profit spinoff, raising ethical questions

Curated on Posted on by Stefaan Verhulst

Alexandra Levine at Politico: “Crisis Text Line is one of the world’s most prominent mental health support lines, a tech-driven nonprofit that uses big data and artificial intelligence to help people cope with traumas such as self-harm, emotional abuse and thoughts of suicide.

But the data the charity collects from its online text conversations with people in their darkest moments does not end there: The organization’s for-profit spinoff uses a sliced and repackaged version of that information to create and market customer service software.

Crisis Text Line says any data it shares with that company, Loris.ai, has been wholly “anonymized,” stripped of any details that could be used to identify people who contacted the helpline in distress. Both entities say their goal is to improve the world — in Loris’ case, by making “customer support more human, empathetic, and scalable.”

In turn, Loris has pledged to share some of its revenue with Crisis Text Line. The nonprofit also holds an ownership stake in the company, and the two entities shared the same CEO for at least a year and a half. The two call their relationship a model for how commercial enterprises can help charitable endeavors thrive…(More).”

COVID-19 interventions: what behavioural scientists should – and shouldn’t – be advising government on

Curated on Posted on by Stefaan Verhulst

Article by Adam Oliver: “Behavioural scientists study human behaviour, which is complex, with different phenomena driving people in different directions, and with even the same phenomena driving people in different directions depending on timing and context. When it comes to assessing the possible threat of a pandemic at its beginning, behavioural scientists simply cannot predict with any degree of accuracy whether or not people are over or underreacting. That said, behavioural scientists do have a potentially important role to play in any present and future infectious disease pandemic response, but first I will expand a little on those aspects of a pandemic where their advice is perhaps a little more circumspect.

Scientific expertise is normally focussed within very specific domains, and yet the relevant outcomes – health, social, and economic-related – of an event such as a pandemic involve considerations that extend far beyond the range of any individual’s area of competence. The pronouncements from a behavioural scientist on whether a government ought to impose policies with such far reaching implications as a national lockdown should thus be treated with a healthy degree of scepticism. To use an analogy, if a person experiences a problem with his or her car and doesn’t possess the skills to fix it, s/he will seek the expertise of a motor mechanic. However, this does not mean that a mechanic has the requisite skills to manage effectively General Motors…

My suggestion is for behavioural scientists to leave the judgments on which interventions ought to be introduced to those appointed to balance all relevant considerations, and instead focus on assessing how the introduced interventions might be made more effective with input from their knowledge of behavioural science. There are, of course, many domains of policy – indeed, perhaps all domains of policy – where behavioural science expertise can be usefully deployed in this way, including in relation to interventions intended to get the economy moving again, in securing volunteering behaviours to help the vulnerable, to encourage people to report and escape from domestic abuse, etc. But in terms of assessing policy effectiveness, perhaps the most visible ways in which behavioural scientists have thus far been involved in the pandemic response is in relation to interventions intended to limit the spread of, and enhance resistance to, the virus: i.e. handwashing, social distancing, mask wearing, voluntary testing, and vaccine uptake….(More)”.

Making data for good better

Curated on Posted on by Stefaan Verhulst

Article by Caroline Buckee, Satchit Balsari, and Andrew Schroeder: “…Despite the long standing excitement about the potential for digital tools, Big Data and AI to transform our lives, these innovations–with some exceptions–have so far had little impact on the greatest public health emergency of our time.

Attempts to use digital data streams to rapidly produce public health insights that were not only relevant for local contexts in cities and countries around the world, but also available to decision makers who needed them, exposed enormous gaps across the translational pipeline. The insights from novel data streams which could help drive precise, impactful health programs, and bring effective aid to communities, found limited use among public health and emergency response systems. We share here our experience from the COVID-19 Mobility Data Network (CMDN), now Crisis Ready (crisisready.io), a global collaboration of researchers, mostly infectious disease epidemiologists and data scientists, who served as trusted intermediaries between technology companies willing to share vast amounts of digital data, and policy makers, struggling to incorporate insights from these novel data streams into their decision making. Through our experience with the Network, and using human mobility data as an illustrative example, we recognize three sets of barriers to the successful application of large digital datasets for public good.

First, in the absence of pre-established working relationships with technology companies and data brokers, the data remain primarily confined within private circuits of ownership and control. During the pandemic, data sharing agreements between large technology companies and researchers were hastily cobbled together, often without the right kind of domain expertise in the mix. Second, the lack of standardization, interoperability and information on the uncertainty and biases associated with these data, necessitated complex analytical processing by highly specialized domain experts. And finally, local public health departments, understandably unfamiliar with these novel data streams, had neither the bandwidth nor the expertise to sift noise from signal. Ultimately, most efforts did not yield consistently useful information for decision making, particularly in low resource settings, where capacity limitations in the public sector are most acute…(More)”.

Trove of unique health data sets could help AI predict medical conditions earlier

Curated on Posted on by Stefaan Verhulst

Madhumita Murgia at the Financial Times: “…Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, launched Nightingale Open Science last month — a treasure trove of unique medical data sets, each curated around an unsolved medical mystery that artificial intelligence could help to solve.

The data sets, released after the project received $2m of funding from former Google chief executive Eric Schmidt, could help to train computer algorithms to predict medical conditions earlier, triage better and save lives.

The data include 40 terabytes of medical imagery, such as X-rays, electrocardiogram waveforms and pathology specimens, from patients with a range of conditions, including high-risk breast cancer, sudden cardiac arrest, fractures and Covid-19. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer and whether it resulted in death, or whether a Covid patient needed a ventilator.

Obermeyer has made the data sets free to use and mainly worked with hospitals in the US and Taiwan to build them over two years. He plans to expand this to Kenya and Lebanon in the coming months to reflect as much medical diversity as possible.

“Nothing exists like it,” said Obermeyer, who announced the new project in December alongside colleagues at NeurIPS, the global academic conference for artificial intelligence. “What sets this apart from anything available online is the data sets are labelled with the ‘ground truth’, which means with what really happened to a patient and not just a doctor’s opinion.”…

The Nightingale data sets were among dozens proposed this year at NeurIPS.

Other projects included a speech data set of Mandarin and eight subdialects recorded by 27,000 speakers in 34 cities in China; the largest audio data set of Covid respiratory sounds, such as breathing, coughing and voice recordings, from more than 36,000 participants to help screen for the disease; and a data set of satellite images covering the entire country of South Africa from 2006 to 2017, divided and labelled by neighbourhood, to study the social effects of spatial apartheid.

Elaine Nsoesie, a computational epidemiologist at the Boston University School of Public Health, said new types of data could also help with studying the spread of diseases in diverse locations, as people from different cultures react differently to illnesses.

She said her grandmother in Cameroon, for example, might think differently than Americans do about health. “If someone had an influenza-like illness in Cameroon, they may be looking for traditional, herbal treatments or home remedies, compared to drugs or different home remedies in the US.”

Computer scientists Serena Yeung and Joaquin Vanschoren, who proposed that research to build new data sets should be exchanged at NeurIPS, pointed out that the vast majority of the AI community still cannot find good data sets to evaluate their algorithms. This meant that AI researchers were still turning to data that were potentially “plagued with bias”, they said. “There are no good models without good data.”…(More)”.