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How these US schools reopened without sparking a covid outbreak

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How these US schools reopened without sparking a covid outbreak


“Cleaning high-touch areas is very important in schools,” Cogan said. But mask-wearing, physical distancing, vaccinations, and other measures are “higher protective factors.”

8. Give agency to parents and teachers in protecting their kids.

Last school year, many districts used temperature checks and symptom screenings as an attempt to catch infected students before they gave the coronavirus to others. But in Austin, Indiana, such formalized screenings proved less useful than teachers’ and parents’ intuition. Instructors could identify when a student wasn’t feeling well and ask them to go see the nurse, even if that student passed a temperature check.

Jetelina said that teachers and parents can both act as a layer of protection, stopping a sick child from entering the classroom. “Parents are pretty good at understanding the symptoms of their kids and the health of their kids,” she said.

In Andrews, Texas, district administrators provided parents with information on covid symptoms and entrusted those parents to determine when a child may need to stay home from school. The Texas district may have “gone way overboard with giving parents agency,” though, Cogan said, in allowing students to opt out of quarantines and mask-wearing—echoing concerns from the Andrews County public health department.

9. We need more granular data to drive school policies.

Throughout the pandemic, I’ve consistently called out a lack of detailed public data on covid-19 cases in schools. The federal government still does not provide such data, and most states offer scattered numbers that don’t provide crucial context for cases (such as in-person enrollment or testing figures). Without these numbers, it is difficult to compare school districts and identify success stories.

My research on school reopenings illuminated another data issue: most states are not providing any covid-19 metrics down to the individual district, making it hard for school leaders to know when they must tighten down on or loosen safety protocols. At the tiny Port Orford–Langlois district in Oregon, for example, administrators had to rely on covid-19 numbers for their overall county. Even though the district had zero cases in fall 2020, it wasn’t able to bring older students back in person until the spring because outbreaks in another part of the county drove up case numbers. Cogan has observed similar issues in New Jersey.

At a local level, school districts may work with their local public health departments to get the data they need for more informed decision-making, Jetelina said. But at a larger, systemic level, getting granular covid-19 data is more difficult—a job for the federal government.

10. Invest in school staff and invite their contributions to safety strategies.

School staff described working long hours, familiarizing themselves with the science of covid-19, and exercising immense determination and creativity to provide their students with a decent school experience. Teaching is typically a challenging job, but in the last 18 months, it has become heroic—even though many people outside school environments take this work for granted, Jetelina said.

Districts can thank their staff by giving them a say in school safety decisions, Cogan recommended. “Educators—they’ve had a God-awful time and had a lot more put on them,” she said. But “every single person that works in a school has as well.” That includes custodians, cafeteria workers, and—crucially—school nurses, who Cogan calls the “chief wellness officers” of the school.

11. Allow students and staff the space to process pandemic hardship.

About 117,000 children in the US have lost one or both parents during the pandemic, according to research from Imperial College London. Thousands more have lost other relatives, mentors, and friends—while millions of children have faced job loss in their families, food and housing insecurity, and other hardships. Even if a school district has all the right safety logistics, school staff cannot truly support students unless they allow time and space to process the trauma that they’ve faced.

P.S. 705 in Brooklyn may serve as a model for this practice. School staff preemptively reached out to families when a student missed class, offering support: “705 is just the kind of place where it is a ‘wrap your arms around the whole family’ kind of a school,” one parent said.

On the first day of school in September 2021—when many students returned in person for the first time since spring 2020—the school held a moment of silence for loved ones that the school community has lost.

New challenges ahead

These lessons are drawn from school communities that were successful in the 2020-2021 school year, before the delta variant hit the US. This highly transmissible strain of the virus poses new challenges for the fall 2021 semester. The data analysis underlying this project led me to profile primarily rural communities, which may have gotten lucky with low covid-19 case numbers in previous phases of the pandemic—but are now unable to escape delta. For example, the Oregon county including Port Orford–Langlois saw its highest case rates yet in August 2021.

The delta challenge is multiplied by increasing polarization over masks, vaccines, and other safety measures. Still, Jetelina pointed out that there are also “a ton of champions out there,” referring to parents, teachers, public health experts, and others who continue to learn from past school reopening experiences—and advocate for their communities to do a better job.

The Solutions Journalism Network supported this project with a reporting grant, as well as trainings and other guidance. Learn more about the five school communities I profiled in this project for the COVID-19 Data Dispatch.

This story is part of the Pandemic Technology Project, supported by The Rockefeller Foundation.

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Facebook wants machines to see the world through our eyes

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Facebook wants machines to see the world through our eyes


For the last two years, Facebook AI Research (FAIR) has worked with 13 universities around the world to assemble the largest ever data set of first-person video—specifically to train deep-learning image-recognition models. AIs trained on the data set will be better at controlling robots that interact with people, or interpreting images from smart glasses. “Machines will be able to help us in our daily lives only if they really understand the world through our eyes,” says Kristen Grauman at FAIR, who leads the project.

Such tech could support people who need assistance around the home, or guide people in tasks they are learning to complete. “The video in this data set is much closer to how humans observe the world,” says Michael Ryoo, a computer vision researcher at Google Brain and Stony Brook University in New York, who is not involved in Ego4D.

But the potential misuses are clear and worrying. The research is funded by Facebook, a social media giant that has recently been accused in the US Senate of putting profits over people’s well-being—as corroborated by MIT Technology Review’s own investigations.

The business model of Facebook, and other Big Tech companies, is to wring as much data as possible from people’s online behavior and sell it to advertisers. The AI outlined in the project could extend that reach to people’s everyday offline behavior, revealing what objects are around your home, what activities you enjoyed, who you spent time with, and even where your gaze lingered—an unprecedented degree of personal information.

“There’s work on privacy that needs to be done as you take this out of the world of exploratory research and into something that’s a product,” says Grauman. “That work could even be inspired by this project.”

FACEBOOK

The biggest previous data set of first-person video consists of 100 hours of footage of people in the kitchen. The Ego4D data set consists of 3,025 hours of video recorded by 855 people in 73 different locations across nine countries (US, UK, India, Japan, Italy, Singapore, Saudi Arabia, Colombia, and Rwanda).

The participants had different ages and backgrounds; some were recruited for their visually interesting occupations, such as bakers, mechanics, carpenters, and landscapers.

Previous data sets typically consisted of semi-scripted video clips only a few seconds long. For Ego4D, participants wore head-mounted cameras for up to 10 hours at a time and captured first-person video of unscripted daily activities, including walking along a street, reading, doing laundry, shopping, playing with pets, playing board games, and interacting with other people. Some of the footage also includes audio, data about where the participants’ gaze was focused, and multiple perspectives on the same scene. It’s the first data set of its kind, says Ryoo.

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This NASA spacecraft is on its way to Jupiter’s mysterious asteroid swarms

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This NASA spacecraft is on its way to Jupiter’s mysterious asteroid swarms


Lucy will take black-and-white and color images, and use a diamond beam splitter to shine far-infrared light at the asteroids to take their temperature and make maps of their surface. It will also collect other measurements as it flies by. This data could help scientists understand how the planets may have formed.

Sarah Dodson-Robinson, an assistant professor of physics and astronomy at the University of Delaware, says Lucy could offer a definitive time line for not only when the planets originally formed, but where.

“If you can nail down when the Trojan asteroids formed, then you have some information about when did Jupiter form, and can start asking questions like ‘Where did Jupiter go in the solar system?’” she says. “Because it wasn’t always where it is now. It’s moved around.”

And to determine the asteroids’ ages, the spacecraft will search for surface craters that may be no bigger than a football field. 

“[The Trojans] haven’t had nearly as much colliding and breaking as some of the other asteroids that are nearer to us,” says Dodson-Robinson. “We’re potentially getting a look at some of these asteroids like they were shortly after they formed.”

On its 4-billion-mile journey, Lucy will receive three gravity assists from Earth, which will involve using the planet’s gravitational force to change the spacecraft’s trajectory without depleting its resources. Coralie Adam, deputy navigation team chief for the Lucy mission, says each push will increase the spacecraft’s velocity from 200 miles per hour to over 11,000 mph.

“If not for this Earth gravity assist, it would take five times the amount of fuel—or three metric tons—to reach Lucy’s target, which would make the mission unfeasible,” said Adam during an engineering media briefing also held on October 14.

Lucy’s mission is slated to end in 2033, but some NASA officials already feel confident that the spacecraft will last far longer. “There will be a good amount of fuel left onboard,” said Adam. “After the final encounter with the binary asteroids, as long as the spacecraft is healthy, we plan to propose to NASA to do an extended mission and explore more Trojans.”

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Reimagining our pandemic problems with the mindset of an engineer

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Reimagining our pandemic problems with the mindset of an engineer


The last 20 months turned every dog into an amateur epidemiologist and statistician. Meanwhile, a group of bona fide epidemiologists and statisticians came to believe that pandemic problems might be more effectively solved by adopting the mindset of an engineer: that is, focusing on pragmatic problem-solving with an iterative, adaptive strategy to make things work.

In a recent essay, “Accounting for uncertainty during a pandemic,” the researchers reflect on their roles during a public health emergency and on how they could be better prepared for the next crisis. The answer, they write, may lie in reimagining epidemiology with more of an engineering perspective and less of a “pure science” perspective.

Epidemiological research informs public health policy and its inherently applied mandate for prevention and protection. But the right balance between pure research results and pragmatic solutions proved alarmingly elusive during the pandemic.

We have to make practical decisions, so how much does the uncertainty really matter?

Seth Guikema

“I always imagined that in this kind of emergency, epidemiologists would be useful people,” Jon Zelner, a coauthor of the essay, says. “But our role has been more complex and more poorly defined than I had expected at the outset of the pandemic.” An infectious disease modeler and social epidemiologist at the University of Michigan, Zelner witnessed an “insane proliferation” of research papers, “many with very little thought about what any of it really meant in terms of having a positive impact.”

“There were a number of missed opportunities,” Zelner says—caused by missing links between the ideas and tools epidemiologists proposed and the world they were meant to help.

Giving up on certainty

Coauthor Andrew Gelman, a statistician and political scientist at Columbia University, set out “the bigger picture” in the essay’s introduction. He likened the pandemic’s outbreak of amateur epidemiologists to the way war makes every citizen into an amateur geographer and tactician: “Instead of maps with colored pins, we have charts of exposure and death counts; people on the street argue about infection fatality rates and herd immunity the way they might have debated wartime strategies and alliances in the past.”

And along with all the data and public discourse—Are masks still necessary? How long will vaccine protection last?—came the barrage of uncertainty.

In trying to understand what just happened and what went wrong, the researchers (who also included Ruth Etzioni at the University of Washington and Julien Riou at the University of Bern) conducted something of a reenactment. They examined the tools used to tackle challenges such as estimating the rate of transmission from person to person and the number of cases circulating in a population at any given time. They assessed everything from data collection (the quality of data and its interpretation were arguably the biggest challenges of the pandemic) to model design to statistical analysis, as well as communication, decision-making, and trust. “Uncertainty is present at each step,” they wrote.

And yet, Gelman says, the analysis still “doesn’t quite express enough of the confusion I went through during those early months.”

One tactic against all the uncertainty is statistics. Gelman thinks of statistics as “mathematical engineering”—methods and tools that are as much about measurement as discovery. The statistical sciences attempt to illuminate what’s going on in the world, with a spotlight on variation and uncertainty. When new evidence arrives, it should generate an iterative process that gradually refines previous knowledge and hones certainty.

Good science is humble and capable of refining itself in the face of uncertainty.

Marc Lipsitch

Susan Holmes, a statistician at Stanford who was not involved in this research, also sees parallels with the engineering mindset. “An engineer is always updating their picture,” she says—revising as new data and tools become available. In tackling a problem, an engineer offers a first-order approximation (blurry), then a second-order approximation (more focused), and so on.

Gelman, however, has previously warned that statistical science can be deployed as a machine for “laundering uncertainty”—deliberately or not, crappy (uncertain) data are rolled together and made to seem convincing (certain). Statistics wielded against uncertainties “are all too often sold as a sort of alchemy that will transform these uncertainties into certainty.”

We witnessed this during the pandemic. Drowning in upheaval and unknowns, epidemiologists and statisticians—amateur and expert alike—grasped for something solid as they tried to stay afloat. But as Gelman points out, wanting certainty during a pandemic is inappropriate and unrealistic. “Premature certainty has been part of the challenge of decisions in the pandemic,” he says. “This jumping around between uncertainty and certainty has caused a lot of problems.”

Letting go of the desire for certainty can be liberating, he says. And this, in part, is where the engineering perspective comes in.

A tinkering mindset

For Seth Guikema, co-director of the Center for Risk Analysis and Informed Decision Engineering at the University of Michigan (and a collaborator of Zelner’s on other projects), a key aspect of the engineering approach is diving into the uncertainty, analyzing the mess, and then taking a step back, with the perspective “We have to make practical decisions, so how much does the uncertainty really matter?” Because if there’s a lot of uncertainty—and if the uncertainty changes what the optimal decisions are, or even what the good decisions are—then that’s important to know, says Guikema. “But if it doesn’t really affect what my best decisions are, then it’s less critical.”

For instance, increasing SARS-CoV-2 vaccination coverage across the population is one scenario in which even if there is some uncertainty regarding exactly how many cases or deaths vaccination will prevent, the fact that it is highly likely to decrease both, with few adverse effects, is motivation enough to decide that a large-scale vaccination program is a good idea.

An engineer is always updating their picture.

Susan Holmes

Engineers, Holmes points out, are also very good at breaking problems down into critical pieces, applying carefully selected tools, and optimizing for solutions under constraints. With a team of engineers building a bridge, there is a specialist in cement and a specialist in steel, a wind engineer and a structural engineer. “All the different specialties work together,” she says.

For Zelner, the notion of epidemiology as an engineering discipline is something he  picked up from his father, a mechanical engineer who started his own company designing health-care facilities. Drawing on a childhood full of building and fixing things, his engineering mindset involves tinkering—refining a transmission model, for instance, in response to a moving target.

“Often these problems require iterative solutions, where you’re making changes in response to what does or doesn’t work,” he says. “You continue to update what you’re doing as more data comes in and you see the successes and failures of your approach. To me, that’s very different—and better suited to the complex, non-stationary problems that define public health—than the kind of static one-and-done image a lot of people have of academic science, where you have a big idea, test it, and your result is preserved in amber for all time.” 

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