Fast Company reporter Mark Sullivan writes that Prof. John Bush and Prof. Martin Z. Bazant have developed a mathematical model that “simulates the fluid dynamics of virus-loaded respiratory droplets in any space, from a cozy kitchen to a gigantic concert hall.”
A new study by MIT researchers finds that “changes in coronavirus levels in wastewater preceded rises and falls in positive test results by four to 10 days,” reports Kim Tingley for The New York Times. The findings suggest that “sewage surveillance could play an important role in helping contain the pandemic.”
Fast Company reporter Adele Peters spotlights Particles for Humanity, an MIT spinoff that is developing a new technology that makes it possible to deliver multiple doses of a vaccine in one shot. “The new technology works like traditional drug delivery,” writes Peters, “but with the addition of tiny time-release capsules filled with antigens, the part of the vaccine that stimulates the immune system so that it can later respond to a virus.”
Prof. Eric Alm speaks with New York Times Magazine reporter Kim Tingley about how studying wastewater can provide public health officials with advance warning of an uptick in coronavirus cases. “If you want to really understand what’s going on in a city on a basic chemical, biological level, you should be looking at the wastewater," says Alm.
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.
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.
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.”
Biobot Analytics, an MIT startup, is testing sewage in regions across the U.S. as part of an effort to detect where the coronavirus is circulating “even before people start showing up at hospitals and clinics and before they start lining up for Covid-19 tests,” writes Maggie fox for CNN.