Reporting for WCVB-TV, Katie Thompson highlights a new study by MIT researchers that examines the role of super-spreading events in the Covid-19 pandemic. "The main idea is that most people generate zero or one cases, but it's the people generating hundreds of cases that we should perhaps be worried about," says postdoc Felix Wong said.
Prof. Yossi Sheffi speaks with Boston 25 reporter Jason Law about how the Covid-19 pandemic is disrupting supply chains. “I don’t think it’s going to be as bad because we are more prepared for this,” says Sheffi of potential impacts caused by the latest rise in Covid-19 cases. “People now in factories and warehouses have dividers that they can work between. Everybody is wearing a mask. People understand the issue better.”
CNBC reporter Greg Iacurci writes that a new paper by members of MIT’s Task Force on the Work of the Future demonstrates how the Covid-19 pandemic has exposed flaws in unemployment benefits for American workers.
Visiting Professor Susan Blumenthal writes for CNN about the need for face mask standards to help stem the spread of Covid-19. “Developing a national certification and labeling system for mask effectiveness, educating about their power for preventing infection, and mandating their use are essential components of protecting individuals and communities from viral spread in America's battle against this pandemic,” writes Blumenthal and her co-author.
Fast Company reporter Adele Peters writes that a new mask developed by Prof. Giovanni Traverso is embedded with sensors that change colors when it is properly positioned. “When you put on the mask, if the edge is in contact with the skin, you will have that temperature change indicating that you have contact,” says Traverso. “If not, then there won’t be that color change, and you can tell immediately.”
Joseph Coughlin, director of the MIT AgeLab, speaks with Jason Bisnoff of Forbes about how financial advisors should stay engaged with their clients during the Covid-19 pandemic. “If you haven’t had these conversations, you have not displayed that you care about these clients and, by the way, this is the new normal,” says Coughlin.
Shigeru Miyagawa, senior associate dean for Open Learning, and instructor Meghan Perdue write for Inside Higher Ed about how the transition to online learning during the Covid-19 pandemic may change how educators teach. "What struck us is that this uncontrolled experiment, as a scientist might put it, may lead to a fundamental change in the way we approach education," they write.
A new study by MIT researchers finds that super-spreading events are larger drivers of the Covid-19 pandemic than originally thought, reports Carey Goldberg for WBUR. “We found in our study that super-spreading events can indeed be a major driver of the current pandemic,” says postdoc Felix Wong. “Most people generate zero or one cases, but it's the people generating hundreds of cases that we really should be worried about.”
A new algorithm developed by MIT researchers could be used to help detect people with Covid-19 by listening to the sound of their coughs, reports Zoe Kleinman for BBC News. “In tests, it achieved a 98.5% success rate among people who had received an official positive coronavirus test result, rising to 100% in those who had no other symptoms,” writes Kleinman.
Mashable reporter Rachel Kraus writes that a new system developed by MIT researchers could be used to help identify patients with Covid-19. Kraus writes that the algorithm can “differentiate the forced coughs of asymptomatic people who have Covid from those of healthy people.”
Quartz reporter Nicolás Rivero highlights a study co-authored by Prof. David Rand that examines the effectiveness of labeling fake news on social media platforms. “I think most people working in this area agree that if you put a warning label on something, that will make people believe and share it less,” says Rand. “But most stuff doesn’t get labeled, so that’s a major practical limitation of this approach.”
A new took developed by MIT researchers uses neural networks to help identify Covid-19, reports Alyse Stanley for Gizmodo. The model “can detect the subtle changes in a person’s cough that indicate whether they’re infected, even if they don’t have any other symptoms,” Stanley explains.
TechCrunch reporter Devin Coldewey writes that MIT researchers have built a new AI model that can help detect Covid-19 by listening to the sound of a person’s cough. “The tool is detecting features that allow it to discriminate the subjects that have COVID from the ones that don’t,” explains Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.
MIT researchers have developed a new AI model that could help identify people with asymptomatic Covid-19 based on the sound of their cough, reports CBS Boston. The researchers hope that in the future the model could be used to help create an app that serves as a “noninvasive prescreening tool to figure out who is likely to have the coronavirus.”
Fox News reporter Kayla Rivas features Prof. Richard Larson’s work developing a new algorithm that could be used to help more accurately pinpoint sources of Covid-19 infections in sewer systems. The algorithm could be used to help “toggle between normal testing to an emergency schedule to locate asymptomatic cases fast before they infect others.”