Coronavirus Innovation Map

Curated on Posted on by Stefaan Verhulst

“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)”

The Fate of the News in the Age of the Coronavirus

Curated on Posted on by Stefaan Verhulst

Michael Luo at the New Yorker: “The shift to paywalls has been a boon for quality journalism. Instead of chasing trends on search engines and social media, subscription-based publications can focus on producing journalism worth paying for, which has meant investments in original reporting of all kinds. A small club of élite publications has now found a sustainable way to support its journalism, through readers instead of advertisers. The Times and the Post, in particular, have thrived in the Trump era. So have subscription-driven startups, such as The Information, which covers the tech industry and charges three hundred and ninety-nine dollars a year. Meanwhile, many of the free-to-read outlets still dependent on ad revenue—including former darlings of the digital-media revolution, such as BuzzFeed, Vice, HuffPost, Mic, Mashable, and the titles under Vox Media—have labored to find viable business models.

Many of these companies attracted hundreds of millions of dollars in venture funding, and built sizable newsrooms. Even so, they’ve struggled to succeed as businesses, in part because Google and Facebook take in the bulk of the revenue derived from digital advertising. Some sites have been forced to shutter; others have slashed their staffs and scaled back their journalistic ambitions. There are free digital news sites that continue to attract outsized audiences: CNN and Fox News, for instance, each draw well over a hundred million visitors a month. But the news on these sites tends to be commodified. Velocity is the priority, not complexity and depth.

A robust, independent press is widely understood to be an essential part of a functioning democracy. It helps keep citizens informed; it also serves as a bulwark against the rumors, half-truths, and propaganda that are rife on digital platforms. It’s a problem, therefore, when the majority of the highest-quality journalism is behind a paywall. In recent weeks, recognizing the value of timely, fact-based news during a pandemic, the Times, The Atlantic, the Wall Street Journal, the Washington Post, and other publications—including The New Yorker—have lowered their paywalls for portions of their coronavirus coverage. But it’s unclear how long publishers will stay committed to keeping their paywalls down, as the state of emergency stretches on. The coronavirus crisis promises to engulf every aspect of society, leading to widespread economic dislocations and social disruptions that will test our political processes and institutions in ways far beyond the immediate public-health threat. With the misinformation emanating from the Trump White House, the need for reliable, widely-accessible information and facts is more urgent than ever. Yet the economic shutdown created by the spread of covid-19 promises to decimate advertising revenue, which could doom more digital news outlets and local newspapers.

It’s easy to underestimate the information imbalance in American society. After all, “information” has never felt more easily available. A few keyboard strokes on an Internet search engine instantly connects us to unlimited digital content. On Facebook, Instagram, and other social-media platforms, people who might not be intentionally looking for news encounter it, anyway. And yet the apparent ubiquity of news and information is misleading. Between 2004 and 2018, nearly one in five American newspapers closed; in that time, print newsrooms have shed nearly half of their employees. Digital-native publishers employ just a fraction of the diminished number of journalists who still remain at legacy outlets, and employment in broadcast-TV newsrooms trails that of newspapers. On some level, news is a product manufactured by journalists. Fewer journalists means less news. The tributaries that feed the river of information have been drying up. There are a few mountain springs of quality journalism; most sit behind a paywall.

A report released last year by the Reuters Institute for the Study of Journalism maps the divide that is emerging among news readers. The proportion of people in the United States who pay for online news remains small: just sixteen per cent. Those readers tend to be wealthier, and are more likely to have college degrees; they are also significantly more likely to find news trustworthy. Disparities in the level of trust that people have in their news diets, the data suggests, are likely driven by the quality of the news they are consuming….(More)”.

Responding to COVID-19 with AI and machine learning

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Curated on Posted on by Stefaan Verhulst

Book by Ron Kohavi, Diane Tang, and Ya Xu: “Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions.

Learn how to use the scientific method to evaluate hypotheses using controlled experiments Define key metrics and ideally an Overall Evaluation Criterion Test for trustworthiness of the results and alert experimenters to violated assumptions. Build a scalable platform that lowers the marginal cost of experiments close to zero. Avoid pitfalls like carryover effects and Twyman’s law. Understand how statistical issues play out in practice….(More)”.

A Closer Look at Location Data: Privacy and Pandemics

Curated on Posted on by Stefaan Verhulst

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)”.

Human migration: the big data perspective

Curated on Posted on by Stefaan Verhulst

Alina Sîrbu et al at the International Journal of Data Science and Analytics: “How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants….(More)”.

The Law and Economics of Online Republication

Curated on Posted on by Stefaan Verhulst

Paper by Ronen Perry: “Jerry publishes unlawful content about Newman on Facebook, Elaine shares Jerry’s post, the share automatically turns into a tweet because her Facebook and Twitter accounts are linked, and George immediately retweets it. Should Elaine and George be liable for these republications? The question is neither theoretical nor idiosyncratic. On occasion, it reaches the headlines, as when Jennifer Lawrence’s representatives announced she would sue every person involved in the dissemination, through various online platforms, of her illegally obtained nude pictures. Yet this is only the tip of the iceberg. Numerous potentially offensive items are reposted daily, their exposure expands in widening circles, and they sometimes “go viral.”

This Article is the first to provide a law and economics analysis of the question of liability for online republication. Its main thesis is that liability for republication generates a specter of multiple defendants which might dilute the originator’s liability and undermine its deterrent effect. The Article concludes that, subject to several exceptions and methodological caveats, only the originator should be liable. This seems to be the American rule, as enunciated in Batzel v. Smith and Barrett v. Rosenthal. It stands in stark contrast to the prevalent rules in other Western jurisdictions and has been challenged by scholars on various grounds since its very inception.

The Article unfolds in three Parts. Part I presents the legal framework. It first discusses the rules applicable to republication of self-created content, focusing on the emergence of the single publication rule and its natural extension to online republication. It then turns to republication of third-party content. American law makes a clear-cut distinction between offline republication which gives rise to a new cause of action against the republisher (subject to a few limited exceptions), and online republication which enjoys an almost absolute immunity under § 230 of the Communications Decency Act. Other Western jurisdictions employ more generous republisher liability regimes, which usually require endorsement, a knowing expansion of exposure or repetition.

Part II offers an economic justification for the American model. Law and economics literature has showed that attributing liability for constant indivisible harm to multiple injurers, where each could have single-handedly prevented that harm (“alternative care” settings), leads to dilution of liability. Online republication scenarios often involve multiple tortfeasors. However, they differ from previously analyzed phenomena because they are not alternative care situations, and because the harm—increased by the conduct of each tortfeasor—is not constant and indivisible. Part II argues that neither feature precludes the dilution argument. It explains that the impact of the multiplicity of injurers in the online republication context on liability and deterrence provides a general justification for the American rule. This rule’s relatively low administrative costs afford additional support.

Part III considers the possible limits of the theoretical argument. It maintains that exceptions to the exclusive originator liability rule should be recognized when the originator is unidentifiable or judgment-proof, and when either the republisher’s identity or the republication’s audience was unforeseeable. It also explains that the rule does not preclude liability for positive endorsement with a substantial addition, which constitutes a new original publication, or for the dissemination of illegally obtained content, which is an independent wrong. Lastly, Part III addresses possible challenges to the main argument’s underlying assumptions, namely that liability dilution is a real risk and that it is undesirable….(More)”.

A controlled trial for reproducibility

Curated on Posted on by Stefaan Verhulst

Marc P. Raphael, Paul E. Sheehan & Gary J. Vora at Nature: “In 2016, the US Defense Advanced Research Projects Agency (DARPA) told eight research groups that their proposals had made it through the review gauntlet and would soon get a few million dollars from its Biological Technologies Office (BTO). Along with congratulations, the teams received a reminder that their award came with an unusual requirement — an independent shadow team of scientists tasked with reproducing their results.

Thus began an intense, multi-year controlled trial in reproducibility. Each shadow team consists of three to five researchers, who visit the ‘performer’ team’s laboratory and often host visits themselves. Between 3% and 8% of the programme’s total funds go to this independent validation and verification (IV&V) work. But DARPA has the flexibility and resources for such herculean efforts to assess essential techniques. In one unusual instance, an IV&V laboratory needed a sophisticated US$200,000 microscopy and microfluidic set-up to make an accurate assessment.

These costs are high, but we think they are an essential investment to avoid wasting taxpayers’ money and to advance fundamental research towards beneficial applications. Here, we outline what we’ve learnt from implementing this programme, and how it could be applied more broadly….(More)”.

COVID-19 is creating a democratic deficit – here’s how to reduce it

Curated on Posted on by Stefaan Verhulst

Article by Matt Ryan: “As parliaments around the country move to scale down operations and defer sittings as part of containing COVID-19 people are beginning to ring the accountability alarm bells….

The good news is that we can learn from those parliaments and politicians around the world who have already been trialling new ways of working that go beyond traditional sittings. Leveraging simple and widely available technologies, they are involving more people with more diverse backgrounds in their processes with less reliance on those people being physically present.

Select Committees in the UK Parliament, for example, have used online “evidence checks” to scrutinise the basis for policy. These one-month exercises use targeted outreach and social media strategies to invite comments from knowledgeable stakeholders and members of the public about the rigour of evidence on which a government department’s policy is based. Evidence for departmental policy is summarised in a two-page document and comments publicly displayed in a web forum that resembles a readers’ comments section in an online news article.

In Taiwan, a participatory governance process pioneered by civic rights activists at the behest of a government minister combines large-scale online participation with smaller in-person gatherings to build a “rough consensus” on legislative proposals related to the digital economy before they are introduced. Known as vTaiwan, the process has led to 26 pieces of national legislation dealing with issues such as Uber, telemedicine and online alcohol sales, and has involved 200,000 people.

The government of Mexico City has raised the stakes even higher, involving more than 400,000 people in a process to draft a new constitution. It included a novel partnership between Change.org and the city mayor that enabled residents to create petition-backed proposals which, once they reached a certain threshold of support, bound the mayor to include them in the draft he submitted to a special constitutional assembly.

Processes like these can also offer relief for politicians and parliamentary officials managing the strain of examining an ever-increasing number of issues of greater complexity with limited personnel and budget. Evidence checks provide access to a wider pool of experts who can bolster existing research capacity. vTaiwan helps to find workable ways forward in industries being rapidly transformed by digital technologies. By “crowdsourcing” the city’s constitution, Mexico City’s mayor retained the trust of residents while undertaking reform at a grand scale….(More)”.