In a lab test, two monkeys died from the novel coronavirus. A species that reacts to the virus as humans do may help us find new treatments, but it’s a weighty task.
A model developed by Boston startup Edmit finds that more than a third of private four-year colleges in the United States are at a high risk financially.
“Many colleges will be able to help students find ways to survive this crisis, but others will need to make the incredibly difficult decision to seek a merger or close in the next few years,” co-founder Nick Ducoff said.
The survey analyzed 17 years of revenue from tuition, return on investments, expenses and the size of tuition discounts that 937 colleges offer students. Of those, 345 are at high risk, meaning that if present financial trends continue they would be able to survive six years, at most.
In March 2020, many state and local governments in the United States enacted stay-at-home policies banning mass gatherings, closing schools, and promoting remote working. By analyzing anonymized location data from millions of mobile devices, we quantify how much people have reduced their daily mobility and physical contacts in accordance with these guidelines. At the regional level, we measure declines in daily commute volume as well as transit between major urban areas. At the individual level, we measure changes in the average user’s daily range of mobility, number of unique contacts, and number of co-location events. According to these five measures, we estimate that the average person in the United States had reduced their daily mobility by between 45-55% as of late April, 2020, and had reduced their daily contacts between 65-75%. The United States’ physical distancing guidelines expired on April 30, 2020 and are not set to be renewed; as of early May, 2020, we report increases in mobility and contact patterns across most states (up to 10-14%, compared to the last week of April), though we do not observe a commensurate increase in commute volume. The response to the COVID-19 pandemic has amounted to one of the largest disruptions of economic, social, and mobility behavior in history, and quantifying these disruptions is vital for forecasting the further spread of this pandemic and crafting our collective response.
When I conceived my first editorial title this year, “2020: A Very Busy Year for Data Science (and Scientists),” COVID-19 was an unknown term to me. Had I been given any hint of its potential for tormenting the human society, I might have at least contemplated the possibility of ‘an unexpected year for data science.’ Indeed, not just the challenges but also the opportunities fall into the category of the unexpected.
All COVID-19-related challenges are virtually the same in nature: a massive stress test on a global scale. The pandemic pushes essentially every system to its extreme, exposing the good, the bad, and the ugly of its inner workings. It tests each system’s resilience (or lack thereof) as a well-functioning integrant of the human ecosystem. We have seen this stress test being conducted in public health and medical systems; in social and economic systems; in political and administrative systems; in law enforcement and regulatory systems; in educational systems; in production and supply chains; and in tourism and service industries, just to name a few of the most obvious ones. I am sure that each of you have your own extensive list, which may vary a great deal depending on your locality, in the physical, cultural, and social senses of the word.
Thinking more positively, the mantra ‘never waste a crisis’ exhorts us to make the best out of the unprecedented global experiments foisted upon us by COVID-19.
Researchers are finding evidence that patients who test positive for the coronavirus after recovering aren’t capable of transmitting the infection, and could have the antibodies that prevent them from falling sick again.
Scientists from the Korean Centers for Disease Control and Prevention studied 285 Covid-19 survivors who had tested positive for the coronavirus after their illness had apparently resolved, as indicated by a previous negative test result. The so-called re-positive patients weren’t found to have spread any lingering infection, and virus samples collected from them couldn’t be grown in culture, indicating the patients were shedding non-infectious or dead virus particles.
The National Institute of Standards and Technology has awarded $20 million in renewed funding to the Center for Statistics and Applications in Forensic Evidence, an interdisciplinary group of more than 60 participants at the University of California, Irvine and five other U.S. institutions of higher education.
Since its initial funding by NIST in 2015, CSAFE has engaged researchers in the analysis and interpretation of forensic evidence – including ballistics, fingerprints, biological samples, shoe markings and digital data – that’s based on sound statistics and solid science. The center’s work is intended to benefit the justice and legal communities and law enforcement officials from the local to national levels.
The Sam M. Walton College of Business at the University of Arkansas now offers three new graduate degrees: Master of Professional Accounting, Master of Applied Business Analytics and Master of Science in Supply Chain Management.
Classes will begin in fall 2020 and applications are being accepted.
“The business world needs professionals who can analyze substantial amounts of data and deliver accounting and supply chain solutions at a sophisticated and global level,” said Matt Waller, dean of the Walton College. “Industry demands it, and we are stepping up to meet the demand.”
“31 proposed prediction models are poorly reported, at high risk of bias, and their reported performance is probably optimistic.” “unreliable predictions could cause more harm than benefit in guiding clinical decisions”
“On any given day an ISR analyst for DCGS is virtually drowning in massive amounts of data – from websites, sources, feeds – and they are often also being multi-tasked, asked to prove mission data, intelligence for that data, and to give briefings to commanders and all levels of personnel,” he said. “We wanted to relieve that data burden by automating some of the processes for viewing, searching and assimilating that data by using real-time models and algorithms.”
Researchers from [Air Force Research Lab], led by Morgan Bishop, senior computer scientist and AI/ML lead, looked at having an enterprise solution to enable a “supply chain of algorithms.”
“As we knew one size does not fit all, we wanted to be able to support a variety of mission sets,” he said. “We are using an AI process called deep learning, where the video pipeline is ingested into models so it can process the video for specific end user needs, making the analyst more effective from the onset.”
The Department of Defense’s Joint Artificial Intelligence Center will get $800 million worth of warfighting AI-enabled technology from Booz Allen Hamilton, the center announced Monday.
Booz Allen Hamilton will be working on the JAIC’s Joint Warfighting mission initiative, the center’s push to leverage AI’s use in the battlefield. The contract is the largest the young center has awarded to date. The work that will take place for the next five years under the task order will cross the “full spectrum of technical support” and deliver AI-enabled systems to the JAIC, according the announcement.
COVID-19 doesn’t create cookie-cutter infections. Some people have extremely mild cases while others find themselves fighting for their lives.
Clinicians are working with limited resources against a disease that is very hard to predict. Knowing which patients are most likely to develop severe cases could help guide clinicians during this pandemic.
We are two researchers at New York University that study predictive analytics and infectious diseases. In early January, we realized that it was very possible the new coronavirus in China was going to make its way to New York, and we wanted to develop a tool to help clinicians deal with the incoming surge of cases. We thought predictive analytics—a form of artificial intelligence—would be a good technology for this job.
A young UK startup is collaborating on a research study with Mayo Clinic to use an FDA-cleared automated echocardiogram assessment and quantification tool to unlock the mysteries of how Covid-19 attacks the heart.
World Economic Forum, World Bank, Somik Lall and Sameh Wahba
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In regions such as Africa, South Asia, and Central America, the pandemic has yet to peak. Their cities, especially the densest ones, will face a great challenge, given their weak infrastructure and limited medical and financial resources. To stave off this crisis, emerging hotspots must be anticipated so that medical and civil resources can be targeted to limit diffusion into surrounding areas. Vulnerable groups need to be identified in advance, so that they can be supported to weather the storm.
To help city leaders prioritize resources towards places with the highest exposure and contagion risk, the World Bank has developed a methodology that can be rapidly deployed. This methodology identifies hotspots for exposure and vulnerability, based on:
The practical inability for keeping people apart, based on a combination of population density and livable floor space that does not allow for 2 meters of physical distancing.
Conditions where, even under lockdown, people might have little option but to cluster (e.g., to access public toilets and water pumps).
Online June 5, starting at 12 p.m. EDT. “This is going to be lots of fun. If you’ve ever wondered how to get started contributing to open source, this is a great way to learn!” Application required.
Online May 21, starting at 10 a.m. PDT. “Join us this week for our ADSA Diversity, Equity, and Inclusion SIG meeting. Hear from Amy Wagler (UT El Paso) and Pamela Scott-Johnson (CSU Los Angeles) about DEI initiatives in their data science programs.” [registration required]
Online June 14-19. “At this year’s ACM SIGMOD International Conference on Management of Data, which will be held virtually in June 2020, I will be moderating a startups panel. To my knowledge, this is the first time such a panel will be hosted in a research-oriented database conference. I would like to give here all interested parties some details about the panel, invite numerous people to tune into the panel and also pose questions/points for the panelists to address by commenting at the end of this note.” [registration required]
Online “The joint conferences of Eurographics and Eurovis 2020 will be held from May 25 till 29 in a virtual forum using a video conferencing platform.” [registration required]
“Can you create a digital tool supporting the health care system (including but not limited to providers, government, and public health and community organizations) during a large-scale health crisis (pandemic, natural disaster, or other public health emergency)?” Deadline for Phase I submissions is June 12.
There’s burnout, and then there’s pandemic-induced burnout. For many workers, the professional environment has changed radically since Covid-19 disrupted life—but the intensity of their jobs hasn’t. Juggling full-time responsibilities, family life, and the stress of confinement makes the risk of burnout greater than ever.
Digital anthropologist and author Rahaf Harfoush, whose book Hustle & Float: Reclaim Your Creativity and Thrive in a World Obsessed With Work investigated the epidemic of burnout, says solutions must go beyond treating symptoms such as exhaustion and anxiety. Instead of merely prescribing rest, exercise, and healthful eating, she says it’s time to deconstruct the underlying cultural sources of burnout and do something radical: Work less. Here, she explains how.
Hugging Face is taking its first step into machine translation this week with the release of more than 1,000 models. Researchers trained models using unsupervised learning and the Open Parallel Corpus (OPUS). OPUS is a project undertaken by the University of Helsinki and global partners to gather and open-source a wide variety of language data sets, particularly for low resource languages. Low resource languages are those with less training data than more commonly used languages like English.