Study: Use of universal mask can save 130,000 lives by February


BAck In April, President Trump chose a single computer model of coronovirus that was his ornament of choice. Unexpectedly, that simulation had initially estimated Rocer than other algorithms, predicting many fewer Kovid-19 deaths – and its unconventional calculations and fluctuating estimates sharply criticized epidemiologists .

But the statisticians behind it have changed their ways, and their new numbers have been published on Friday, which scientists have been saying for a long time: that social remedial measures can lead to large numbers of deaths. , And wearing a mask widely in public saved thousands of lives.

“We think the important point here is that a huge winter boom is coming,” Christopher Murray, a lead author on the paper and director of the University of Washington’s Institute for Health Metrics and Evaluation, said in a press briefing. At this point, the wave is not entirely preventable, but “extending the mask is one of the easiest victories for the United States.”

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Specifically, the paper estimated that by the end of February some half million Kovid-19-related deaths could occur in the US, and about 130,000 of those tragedies could be forested with universal mask use. But experts warn that the figures for any hypothetical scenario of the model are no less useful than comparisons between different probabilities. With those estimates shoulder to shoulder, you can begin to see how much of an impact such as wearing masks can have on the population level, if you face the authors’ assessment, a person’s transition from face covering. The risk of this can be reduced to around 40. %.

“It is impossible to predict the exact number,” said Ruth Etzioni, a biostatistician at the Fred Hutchinson Cancer Research Center and the University of Washington, who was not involved in the new research. “What policy should go on here is the difference between scenarios with and without masks.”

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The initial work of the IHME team last spring was not an attempt to model transmission rates and incubation periods of novel coronoviruses, but rather to spread data from elsewhere in the United States, and on a large scale of increasing cases. With, the bell-like curve showed symmetrical fall. His estimates also skewed a fair amount, and as Nicholas Reich, a University of Massachusetts, Amherst, biostatistician, whose team compares the various Kovid-19 models, told him the stat, “was inaccurate and modeled. Did not reflect the consensus of. Community. ”

IHME then switched to a more traditional method of modeling infectious disease, which aims to calculate the mathematical cascade of transmission: the number of people susceptible to the disease, how many of them are exposed, how many are infected and how well And therefore have at least temporary immunity.

Once the institute made the switch, Reich explained, “Their more recent short-term forecasts, which have been submitted to the COVID-19 forecasting hub, have performed reasonably well in accuracy for up to a month in the future. They are the best models. Are not, but seem to be making precisely short-term predictions.

However, this paper surpasses that one month mark, and the further you go into the future, the greater the uncertainty of estimates. The team used state-by-state data from the past on all types of case rates and other variables — such as cell phone mobility data, seasonal pneumonia fluctuations, mask levels — to project how those Results can be transferred by rotating the variable.

Such analysis is not designed to tell us how effective wearing a mask is in reducing the spread of disease. Rather, it is just one of the many bits of information that researchers feed into their models – and they infer this inference from a meta-analysis they did from previous peer-reviewed studies and preprints Was those who specifically looked at that question.

Nor should we expect this model to give any sort of certainty about what happens in the future.

“It’s not predictive per se, because the results are conditional on very specific model assumptions about how effective masks are and how fast the masks are worn,” Reich said.

Instead, as Zeynep Tufekci put it across the Atlantic, we should use such a study to “prune the destructive branches of a tree of possibilities that lie before us.”

Because all kinds of policy and behavior changes occur at once, it is almost impossible to choose the effect of a single strand. What this kind of analysis can provide is an indication of the general direction we should lead – and which we should avoid – to keep as many people safe as possible. Given that a scenario in which every single person wears a mask in public at all times, resulting in multiple deaths in multiple deaths, in which the use of the mask continues at its anticipated current pace, an effective course of action. Seems very clear, the author says.

As uncertain as their conclusion may be, it may still be useful to say.

“We don’t need a model to tell us that we all should wear masks, we don’t need a model to tell us that if we continue the way we’re going, we’re going to see tens of thousands. There have been deaths within the next few months. “But sometimes when someone offers a model and you look at these curves and you see these numbers, it helps to freak you out appropriately . ”

He said that it could also help in enhancing public health policy decisions: “Such a model could be very important for our governors who are really trying to do something to implement the masked mandate . They are not popular, so politically it can be tough on a governor who is trying to do the right thing. Such models can provide a kind of evidence to support that policy. “

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