According to the CDC, 101 children age 0 to 14 have died from influenza, while 31 children have died from COVID-19.
No evidence exists to support the theory that children pose a threat to educational professionals in a school or classroom setting, but there is a great deal of evidence to support the safety of in-person education.
According to the CDC, 131,332 Americans have died from pneumonia and 121,374 from COVID-19 as of July 11th, 2020.
Had the CDC used its industry standard, Medical Examiners’ and Coroners’ Handbook on Death Registration and Fetal Death Reporting Revision 2003, as it has for all other causes of death for the last 17 years, the COVID-19 fatality count would be approximately 90.2% lower than it currently is.
Could underutilized government offices, empty parking lots, or shuttered public schools help solve your community’s shortage of affordable housing or senior care facilities? Research suggests that it’s entirely possible. The U.S. government alone owns an estimated 45,000 underused or underutilized buildings, plus abundant surplus land. And, as a result of the current pandemic, organizations across the public and private sectors are now recognizing that many of us don’t really need to be in the office every day to get our work done. This underutilized space and property represents enormous untapped value which could be leveraged to finance investments in other areas.
Take the challenge of affordable housing. Today, nearly 40 million Americans cannot afford their current homes – spending as much as half of their incomes on housing. It’s estimated that as many as 7.2 million new affordable housing units are needed to meet demand. What if the public sector could leverage assets they already have to help bridge that gap?
In Canada, various governments have already done just that. By selling more than 240 surplus properties valued at some $120 million, the province of Ontario was able to save almost $10 million in annual operating costs. Some of those properties are now being repurposed for low-income and senior housing. Similarly, the city of Toronto launched an initiative to repurpose 18 city-owned properties into almost 13,000 affordable housing units.
What can we learn from these successes? There are several steps that policymakers and public sector officials — along with multidisciplinary teams of finance, human resources, technology, and corporate real estate stakeholders — should take in order to begin leveraging the untapped potential of unused buildings and property.
When Sir Thomas More coined the term “utopia,” he lifted two words from Ancient Greek that roughly translate into “not a place.” Turns out people from the 16th century still understood satire, perhaps better than we do today. After all, we are the ones operating under the assumption that we can remap society in order to build consequence-free transportation network without a shred of humor to keep us grounded.
We may not need satire in this instance, however. A new study published in the American Journal of Public Health asks questions about how just effectively the shift to autonomy will benefit society as a whole. Industry leaders have broadly framed the shift toward self-driving as kicking down the door to an idyllic universe where no one wants for transportation, with autonomous taxis serving as the first wave of this planned paradise. The reality may be vastly different that what’s being sold, however.
The study essentially asserts that the entire concept of robotic cabs doesn’t actually serve poor communities any better than just buying one’s own automobile. Researchers compared the costs of a robo-taxi trip with those of owning a conventional used vehicle in an urban environment. Tabulating the combined costs of vehicle financing, licensing, insurance, routine maintenance, fuel/electricity and everything else they could account for, the team estimated that self-driving taxis would cost a minimum of $1.58 per mile. By contrast, the total cost associated with traditional vehicle ownership (assuming one is trying to be thrifty) ended up being 52 cents per mile. At least, that was the case for their model in San Francisco.
While your author has long suspected that unsupervised robotic taxis might outpace the subway as one of the dirtiest ways to get around (and become potential liabilities for whoever operates them), the general assumption has been that they’ll offer societal and health benefits that vastly outperform private vehicle ownership — almost as if the people making these assessments have never taken a regular cab or piloted an inner-city ZipCar. Other presumed benefits involve improved air quality by making it easier for people to get by without an automobile of their own.
But this thinking comes with some problems. Studies have already shown that ride-hailing firms exacerbate congestion by having a fleet of cars constantly scouring the streets in search of fares. That interim period between riders wastes energy and will be broadly similar when/if autonomous vehicles arrive. Why should we believe they’ll be any different when they’ll be similarly competing for riders and milling around neighborhoods? Even if they’re entirely electric, that energy has to be sourced from somewhere, and much of it will be in service of nothing.
The neglect of AI ethics extends from universities to industry
A study by data science firm Anaconda found an absence of AI ethics initiatives in both academia and industry.
Amid a growing backlash over AI‘s racial and gender biases, numerous tech giants are launching their own ethics initiatives — of dubious intent.
The schemes are billed as altruistic efforts to make tech serve humanity. But critics argue their main concern is evading regulation and scrutiny through “ethics washing.”
At least we can rely on universities to teach the next generation of computer scientists to make. Right? Apparently not, according to a new survey of 2,360 data science students, academics, and professionals by software firm Anaconda.
Only 15% of instructors and professors said they’re teaching AI ethics, and just 18% of students indicated they’re learning about the subject.
Many people suspect they’ve been infected with COVID-19 by now, despite the fact that only 0.5% of the UK’s population has actually been diagnosed with it. Similar numbers have been reported in other countries. Exactly how many people have actually had it, however, is unclear. There is also uncertainty around what proportion of people who get COVID-19 die as a result, though many models assume it is around 1%.
We believe there has been over-confidence in the reporting of infection prevalence and fatality rate statistics when it comes to COVID-19. Such statistics fail to take account of uncertainties in the data and explanations for these. In our new paper, published in the in the Journal of Risk Research, we developed a computer model that took these uncertainties into account when estimating COVID-19 fatality rates. And we see a very different picture.
You’ve seen the headlines: “Coronavirus Escape: To the Suburbs” in the New York Times, “Coronavirus: Americans flee cities for the suburbs” in USA Today, “Will the Coronavirus Make the Suburbs Popular Again?” in Architectural Digest.
The coronavirus pandemic’s stay-at-home orders have residents of dense urban areas like New York City pondering a permanent move to somewhere more spread-out for obvious reasons: more space, more land, lower prices.
Mulling the decision to leave New York has almost reached cliche status (there’s even a Leaving New York” essay genre, as the Times notes points out).
As more New Yorkers leave, it invites near-constant speculation about a “mass exodus” out of cities. But are the folks skipping town getting outsized attention? Are there really that many people moving away—for good?
Science denialism is not just a simple matter of logic or ignorance
Bemoaning uneven individual and state compliance with public health recommendations, top U.S. COVID-19 adviser Anthony Fauci recently blamed the country’s ineffective pandemic response on an American “anti-science bias.” He called this bias “inconceivable,” because “science is truth.” Fauci compared those discounting the importance of masks and social distancing to “anti-vaxxers” in their “amazing” refusal to listen to science.
It is Fauci’s profession of amazement that amazes me. As well-versed as he is in the science of the coronavirus, he’s overlooking the well-established science of “anti-science bias,” or science denial.
Americans increasingly exist in highly polarized, informationally insulated ideological communities occupying their own information universes.
Within segments of the political blogosphere, global warming is dismissed as either a hoax or so uncertain as to be unworthy of response. Within other geographic or online communities, the science of vaccine safety, fluoridated drinking water and genetically modified foods is distorted or ignored. There is a marked gap in expressed concern over the coronavirus depending on political party affiliation, apparently based in part on partisan disagreements over factual issues like the effectiveness of social distancing or the actual COVID-19 death rate.
In theory, resolving factual disputes should be relatively easy: Just present strong evidence, or evidence of a strong expert consensus. This approach succeeds most of the time, when the issue is, say, the atomic weight of hydrogen.
But things don’t work that way when scientific advice presents a picture that threatens someone’s perceived interests or ideological worldview. In practice, it turns out that one’s political, religious or ethnic identity quite effectively predicts one’s willingness to accept expertise on any given politicized issue.
“Motivated reasoning” is what social scientists call the process of deciding what evidence to accept based on the conclusion one prefers. As I explain in my book, “The Truth About Denial,” this very human tendency applies to all kinds of facts about the physical world, economic history and current events.
The world’s premier health agency pushed a flawed coronavirus containment strategy — until it disappeared from public view one day before the outbreak was declared a pandemic.
On January 17, the world’s most trusted public health agency, the Centers for Disease Control and Prevention, announced it was screening travelers from Wuhan, China, because of a new infectious respiratory illness striking that city.
It was the CDC’s first public briefing on the outbreak, coming as China reported 45 cases of the illness and two deaths linked to a seafood and meat market in Wuhan. Chinese health officials had not yet confirmed that the new illness was transmitted from person to person. But there was reason to believe that it might be: four days earlier, officials in Thailand confirmed their first case, a traveler from Wuhan who had not visited the seafood market.
“Based on the information that CDC has today, we believe the current risk from this virus to the general public is low,” said Nancy Messonnier, the CDC’s director of the National Center for Immunization and Respiratory Diseases. Messonnier, 54, was a veteran of the CDC’s renowned Epidemiological Intelligence Service, where she had risen through the ranks during the national responses to the anthrax attacks and the previous decade’s swine flu pandemic to eventually head the agency’s vaccines center.
Most of the novel coronavirus’s infections apparently went “from animals to people,” she explained, and human transmission was “limited.”
There were many reasons why the information the CDC had on January 17 was wrong. It was wrong because China’s leaders withheld what they already knew about the virus from the World Health Organization. It was wrong, perhaps, because Trump administration officials had cut CDC staffers in Beijing who might have reported the truth directly from China. And it was wrong because past coronavirus outbreaks provided a false guide to an illness new to humanity.
Companies working on self-driving vehicles have criticized an insurance industry study suggesting that only a third of all U.S. road crashes could be prevented by driverless cars, arguing that the study has underestimated the technology’s capabilities.
The study by the Insurance Institute for Highway Safety (IIHS), released on Thursday, analyzed 5,000 U.S. crashes and concluded that likely only those caused by driver perception errors and incapacitation could be prevented by self-driving cars.
The autonomous vehicle industry quickly responded that its cars were programmed to prevent a vastly higher number of potential crash causes, including more complex errors caused by drivers making inadequate or incorrect evasive maneuvers.
While self-driving cars won’t get distracted or drive drunk, that only accounts for a third of wrecks that occur, according to the insurance industry.
Self-driving cars likely have a long, long way to go.
In a blow to hopes for a future free of car crashes with the coming of self-driving cars, a study released Thursday by the Insurance Institute for Highway Safety shows totally driverless cars would have a difficult time achieving such a goal.
The IIHS looked at more than 5,000 police-reported crashes from the National Motor Vehicle Crash Causation Survey, which the insurance industry-funded group said represents vehicle crashes that resulted in one car towed and required emergency medical services.
Combing through the files, the IIHS then sorted the crashes into five categories: sensing and perception; predicting; planning and deciding; execution and performance; and incapacitation errors. Self-driving cars will be able to eliminate sensing and perception errors, or crashes that result in the driver’s distraction, and autonomous technologies won’t be subject to the influence of drugs or alcohol. So, that takes incapacitation errors out. From the sample, that accounts for 34% of crashes. Let’s note the figure is not an insignificant number of crashes automated cars could prevent — 2 million a year in the US alone.
“It’s likely that fully self-driving cars will eventually identify hazards better than people,” said Jessica Cicchino, IIHS vice president for research, “but we found that this alone would not prevent the bulk of crashes.”
Chief Economist on U.S. recovery and how we’re ‘standing at the bottom of the canyon’
She had been working as a concierge services coordinator at a nonprofit performing arts organization in New York City for four years before the closure of entertainment venues across the city destroyed demand for her skills.
“Job hunting is already incredibly tough without a global pandemic,” she told Yahoo Money.
The coronavirus pandemic and response have left millions of Americans like Laura without a job and caused employment income loss for nearly half of the households across the country, according to research from the Household Pulse Survey by the U.S. Census Bureau.
Remote working has meant many people are skipping their morning commute.
COVID-19 has lead to more and more employees working from home.
98% of people surveyed said they would like the option to work remotely for the rest of their careers.
But not everything is positive, with workers finding the biggest challenge is ‘unplugging’ from work.
According to the U.S. Census Bureau, nearly one-third of the U.S. workforce, and half of all “information workers”, are able to work from home. Though the number of people working partially or fully remote has been on the rise for years now, the COVID-19 pandemic may have pressed the fast-forward button on this trend.
With millions of people taking part in this work-from-home experiment, it’s worth asking the question – how do people and companies actually feel about working from home?
