Stefaan Verhulst

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

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

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

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

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

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

Blog by Diana Elliott and Robert Santos: “Social distancing measures to curtail the community spread of COVID-19 have upended daily life. Just before lockdowns were implemented across the country, there was tremendous movement and migration of people relocating to different residences to shelter in place. This makes sense for the people involved but could be disastrous for the communities they fled and the final 2020 Census counts.

Pandemic-based migration undermines an accurate count

The 2020 Census, like most data collected by the US Census Bureau, is residence based. In the years leading up to 2020, the US Census Bureau worked diligently on the quality of the Master Address File, or the catalog of all residential addresses in the country. Staff account for newly built housing developments and buildings, apartment units or accessory dwelling units that are used as permanent residences, and the demolition of homes and apartments in the past decade. Census materials are sent to an address, rather than a person.

Most residences across America have already received their 2020 Census invitation. Whether completed online, by paper, by phone, or in person, the first official question on the 2020 Census questionnaire is “How many people were living or staying in this house, apartment, or mobile home on April 1, 2020?” Households are expected to answer this based on the concept of “usual residence,” or the place where a person lives and sleeps most of the time.

Despite written guidance provided on the 2020 Census on how to answer this question, doing so may be wrought with complexities and nuance from the pandemic.

First, research reveals that respondents do not often read questionnaire instructions; they dive in and start answering. With many people scrambling to other counties, cities, and states to hunker down for the long haul with loved ones, this will lead to incorrect counts when people are counted at temporary addresses.

Second, for many, the concept of “usual residence” has little relevance in the uncertainty unfolding during the COVID-19 pandemic. What if your temporary address becomes your permanent address? What does “usual residence” mean during a global epidemic that could stretch for 18 months or more? And perhaps more importantly, what should it mean?

Finally, there is the added complication of census operational delays (PDF). Self-response to the 2020 Census has been extended into August, as have the nonresponse follow-up efforts, when enumerators knock on the doors of those who haven’t yet answered the census. Additional delays seem unavoidable. The longer the delay, the more time there is for people who have not yet completed a census form to realize their temporary plan has evolved into a state of permanence….(More)”.

NCSU Press Release: “Researchers from North Carolina State University and the Army Research Office have demonstrated a new model of how competing pieces of information spread in online social networks and the Internet of Things (IoT). The findings could be used to disseminate accurate information more quickly, displacing false information about anything from computer security to public health….

In their paper, the researchers show that a network’s size plays a significant role in how quickly “good” information can displace “bad” information. However, a large network is not necessarily better or worse than a small one. Instead, the speed at which good data travels is primarily affected by the network’s structure.

A highly interconnected network can disseminate new data very quickly. And the larger the network, the faster the new data will travel.

However, in networks that are connected primarily by a limited number of key nodes, those nodes serve as bottlenecks. As a result, the larger this type of network is, the slower the new data will travel.

The researchers also identified an algorithm that can be used to assess which point in a network would allow you to spread new data throughout the network most quickly.

“Practically speaking, this could be used to ensure that an IoT network purges old data as quickly as possible and is operating with new, accurate data,” Wenye Wang says.

“But these findings are also applicable to online social networks, and could be used to facilitate the spread of accurate information regarding subjects that affect the public,” says Jie Wang. “For example, we think it could be used to combat misinformation online.”…(More)”

Full paper: “Modeling and Analysis of Conflicting Information Propagation in a Finite Time Horizon,”

“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)”

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)”.

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)”.

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)”.

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)”.

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)”.