Talking about the effectiveness of masks, Bill Nye uses a map and some props to show mask-wearing against infection.
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The University of Oxford’s Blavatnik School of Government defined an index to track containment measures for the coronavirus. For The New York Times, Lauren Leatherby and Rich Harris plotted the index against cases and hospitalizations:
When cases first peaked in the United States in the spring, there was no clear correlation between containment strategies and case counts, because most states enacted similar lockdown policies at the same time. And in New York and some other states, “those lockdowns came too late to prevent a big outbreak, because that’s where the virus hit first,” said Thomas Hale, associate professor of global public policy at the Blavatnik School of Government, who leads the Oxford tracking effort.
A relationship between policies and the outbreak’s severity has become more clear as the pandemic has progressed.
States with more restrictions tend to have lower rates.
From these plots, it seems clear what we need to do. But I think most people have made up their minds already, and the interpretation of the data leads people to different conclusions.
With the holidays coming up, I just hope you lean towards clarity.
Risk of coronavirus infection changes depending on the amount of contagious particles you breathe in. El Pais illustrated the differences when you take certain measures, namely wearing masks, ventilation, and decreased exposure time.
The suggestions are based on statistical models, so there is more uncertainty than I think the explanations provide, but the sequence of illustrations provides a clear picture of what we can do — if you must do things indoors.
The math behind wearing a mask can seem unintuitive at times. Minute Physics and Aatish Bhatia break it down in this illustrated video to show why wearing masks works:
The premise is that there’s a two-way effect with breathing in and breathing out. There are some assumptions here, but there’s an interactive component that lets you adjust the variables. They’ve also made the code available.
Studies suggest that wide adoption of masks can reduce the spread of the coronavirus. A meta-analysis by Ali Mokdad and his research group at the Institute for Health Metrics and Evaluation estimates at least a 30% reduction and up to 50%, which can lead to a big difference, as illustrated by Connie Jin for NPR:
Wear the mask.
NYT’s The Upshot ran a survey through the data firm Dynata asking people how often they wear a mask in public. The Upshot then mapped the likelihood that a random group of five people are all wearing masks:
These variations reflect differences in disease risk and politics, but they also may reflect some local idiosyncrasies. Elizabeth Dorrance Hall, an assistant professor of communications at Michigan State University, said mask behavior can be subject to a kind of peer pressure: If most everyone is wearing one, reluctant people may go along. If few people are, that can influence behavior, too. Such dynamics can shape the behavior of friends, neighbors and communities.
As you might guess, it looks similar to the map of where people were staying at home.