Data Science newsletter – May 26, 2020

GROUP CURATION: N/A

For many of us, our microwaves and dishwashers aren’t the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way.

But we may soon be rethinking that, thanks to new research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called “Sapple,” analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter.

Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used.

In mid-March, just before President Trump declared COVID-19 a national emergency, Stanford psychology professor Robb Willer posted a call to arms on Twitter, asking for suggestions on how the social and behavioral sciences could help to address the pandemic. “What ideas might we have to recommend? What research could we do?” he asked. “All ideas, half-baked or otherwise, are welcome!”

Given the importance of our social interactions to the spread of the pandemic, behavioral sciences should have a lot to tell us. So Willer got a large response, and the result was a huge team effort coordinated by Willer and New York University social psychology professor Jay Van Bavel. The goal: to sum up all the best and most relevant research from psychology, sociology, public health, and other social sciences. Published in the journal Nature Human Behaviour last week—a lightning-fast turnaround for academia—the resulting paper highlights research that addresses behavioral questions that have come up in the pandemic, from understanding cultural differences to minimizing scientific misinformation.

But data sharing—during a pandemic or otherwise—is a complex problem comprising the logistics of sharing the data itself, all the cultural and privacy concerns, and standards and permissions associated with moving data.

Eli Dart took on the logistics questions first. He says the goal is for data to be where it’s productive. Sometimes that means moving the compute to the data, but in some cases moving data can be a huge challenge for researchers. “Now they’re wrangling: how do I get my 20-terabyte dataset over to where it needs to be?,” he said. “And that is just not a good use of scientists’ time.”

The vision is keeping “all the tech in the tech bucket” and letting humans orchestrate that in a way that is productive and scalable. “I don’t want to sort of just be Mr. Science DMZ over here,” Dart said—referring to the network architecture that he has pioneered—“But there’s a set of architectural models that allow us to do that in a way that’s consistently performing.”

Even now, as the world economy slumps into a recession, Jeff Glickman and his boutique investment firm, J4 Capital, are quietly taking gains. “Suffice it to say we’re making a profit in this market,” he says.

This somewhat understates the miracle that Glickman claims to have performed. When we spoke on March 20, J4 Capital was up nearly 4% for the year, according to internal documents Glickman shared, while the Dow Jones Industrial Average was down nearly 27%—a heroic beat of nearly 31 percentage points. Many other hedge funds were down by double digits and teetering. When we spoke again on May 7, he was approaching a 5% return.

What’s certain is the coming few months will say a lot about the state of the country, the public’s psyche and how much death and illness it’s willing to accept. With so much confusing data and contradictory political messaging on the reopening before the November elections, there is a road map to follow this summer to know if the fall will be a time of true recovery or deepening despair.

Here are five story lines this summer that will reveal whether the U.S. has turned the corner:

1. Testing and contact tracing need to grow dramatically

The Wharton School received an anonymous $10 million donation this week to propel research and learning initiatives through the Wharton Neuroscience Initiative.

WiN Director Michael Platt said he hopes to provide funds for postdoctoral fellows and summer research opportunities through this donation, as well as purchase lab infrastructure systems for future WiN research. He also hopes to add more courses on the subject of neuroeconomics to the current Wharton curriculum.

In the race to combat the COVID-19 pandemic, the world’s scientists have embraced a radically new method of disseminating information about their research, offering it quickly and without filters in the effort to understand and control this deadly disease.

But their new communication model is striking at the heart of scientific integrity, publicizing research that has been corrupted by speed, sloppiness and opacity. And now the academic world is being roiled by a question for which millions of lives hang in the balance: Is the public being well-served by the fast and free flow of research — or dangerously misled?

Nowhere is the question over scientific conduct louder than at Stanford University, where a trio of researchers are accused of promoting faulty analysis and “tipping the scale” on antibody studies that they say proves the virus is more widespread and less lethal than we feared, and that public health restrictions are too strict.

And now the university, which has also come under fire, is investigating the veteran professors’ research, a significant step in a world that cherishes credibility and reputation.

Our economy is built on Americans of all class levels buying things. What happens when the ability — and desire — to do so goes away?

Spurred by the COVID-19 pandemic, Princeton researchers have developed a diagnostic tool to analyze chest X-rays for patterns in diseased lungs. The new tool could give doctors valuable information about a patient’s condition, quickly and cheaply, at the point of care.

In this post I use publicly available datasets to analyse European lockdowns up until mid-May. This includes:

A summary of the lockdowns of 28 European countries in terms of the severity of the restrictions imposed, their effect on average mobility, and their impact on cases and deaths.

A comparison of the lockdowns in the hardest hit countries with lockdowns in countries with the fewest COVID-19 deaths; the hardest hit countries entered their lockdowns later and more slowly than those with the lowest death rates, and the former suffered from >15x more deaths per capita than the latter.

An analysis of the the lockdown burden (severity of restrictions x time) experienced by the citizens of different countries to identify lockdowns that may be under or over-powered.

What if the IoT provided us with advanced warning of a pandemic event and nobody was listening? That’s exactly what happened with COVID-19 in the United States, according to Inder Singh, CEO of Kinsa Health, a company that makes connected thermometers.

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Singh made this bold statement on Wednesday during a Stacey On IoT virtual event titled, “Why didn’t the IoT predict COVID-19?” Also on the panel were Dr. Jennifer Radin, an epidemiologist at the Scripps Research Translational Institute, and Dena Mendelsohn, director of health policy and data governance at Elektra Labs, which tests and compiles data on consumer-grade medical devices around what and how they track health information.

Neither Radin or Mendelsohn disagreed with Singh, who subsequently proved his case by sharing Kinsa data from around the country — which is freely available online — that shows body temperature spikes in normal situations. The first sign of an abnormal health situation across the U.S. population appeared in early March, roughly three days before the CDC noted COVID-19 hotpots in the northeast region of the country as well as in Florida.

Covid-19 has changed the way that towns and cities look. It has offered views of public places with fewer cars and cleaner air, roads you can stroll down, cycling without danger. It has made some things seem more precious, such as green spaces and parks. It has renewed appreciation of the social infrastructures of support and care. It has heightened awareness of the ways in which one person’s actions can affect another’s. It has made everyone more conscious of the ways they occupy space in relation to other people.

It has also prompted the idea that big cities have taken a hit from which they won’t fully recover. The virus first appeared in Wuhan, population 11 million, and some of its worst outbreaks have been in New York, London, Milan and São Paulo. Crowds and public transport, goes the theory, are bad for your health. Remote working, boosted by lockdowns, will be here to stay. Balaji Srinivasan, a Silicon Valley venture capitalist summed this view up in a pithy tweet: “Sell city, buy country.”

Oregon State University, the University of Washington and University of Alaska, Fairbanks will receive up to $300 million to lead a new institute focused on climate, ocean and coastal challenges that demand collaboration and sharing of scientific resources.

The National Oceanic and Atmospheric Administration chose the three universities to form the Cooperative Institute for Climate, Ocean and Ecosystem Studies, or CICOES.

“Increasingly, our scientific challenges require novel collaborations that span regional-to-global scales and include the human dimension,” said Roberta Marinelli, dean of OSU’s College of Earth, Ocean, and Atmospheric Sciences. “This new institute will provide a wealth of expertise and innovation to advance the science that underpins NOAA’s mission.”

A team that includes Linfeng Zhang and Roberto Car of Princeton University, US, has conducted ab initio molecular dynamics simulations for up to 100 million atoms, probing timescales up to a few nanoseconds.1 Sure, it’s a long way from the Devs fantasy of an exact replica of reality. But it suggests that simulations with quantum precision are reaching the stage where we can talk not in terms of handfuls of molecules but of bulk matter.

For some COVID-19 patients, the body’s immune response may be as destructive as the virus that causes the disease. The persistent high fevers, severe respiratory distress, and lung damage seen in some critically ill patients are all signs of an immune system in overdrive.

Now, a new clinical trial will test a treatment that targets this overactive immune response, says Howard Hughes Medical Investigator Bert Vogelstein. He and his team at the Johns Hopkins University School of Medicine are currently recruiting individuals for the trial, which includes patients ages 45 to 85 at the Johns Hopkins Hospital who have COVID-19 but who aren’t on a ventilator or in the ICU.

Online June 23-25. “Inc. Magazine says ‘Collision presents the world’s best speakers, and curates content tracks that are as cutting-edge as they are thought-provoking.'”

… “The same colormap applied to a diverse array of data gets monotonous and confusing,” said Rick Saltus, a senior research scientist with the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder. “You’re trying to communicate both effectively and efficiently, and that’s impeded if the viewer is presented with a variety of concepts, all illustrated using identical color mappings.”

Color researchers and visualization experts around the world are working to change this status quo. A number of groups are developing new tools to help scientists image increasingly complex data sets more accurately and intuitively, and with higher fidelity, using context as a guide to ensure an appropriate balance of hue, luminance, and saturation.

Hitting the wine lately? Binge-watching hours of Schitt’s Creek? You’re not alone. Without a commute, many remote employees are logging an additional three hours a day and struggling to healthfully transition from working at home to simply being home. Here are a few strategies to help.

In any startup, there’s tension between doing something manually and building tech to do it automatically. It’s tempting to say, “Let’s write a chatbot program to handle customer service.” But for most startups, it’s probably easier to have a human do it. Especially when testing new features, it’s often more efficient (if a little more boring) to have someone at a keyboard doing the rote work before you spend precious engineering resources building something that people may not actually want. Doing things by hand initially is usually the smarter way to go, until the demand outpaces the manual process. Then, as John Henry learned, automation wins.

But here’s the twist: Your customers often expect a computer to be behind the curtain, and the more your manual process can seem robotic, the more trust your customers will have in you.