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Peak COVID-19 Deaths In U.S. Could Be Further Out Than Predicted, According To UT Model

A message in chalk reminds people to wash their hands and be safe.
Gabriel C. Perez
Exercise equipment is roped off with caution tape at Mueller Lake Park last month.

The number of deaths related to COVID-19 have likely not peaked nationwide, UT Austin researchers reported Friday. Their findings are in contrast to those of a popular COVID-19 predictive model from the University of Washington’s Institute for Health Metrics and Evaluation, which suggested U.S. deaths peaked Monday.

UT researchers say their model is different because it uses geolocation data from cellphones to predict the effect of social distancing in all 50 states. It can be broken down by state, and gives daily forecasts of how many people will die from the disease in the coming weeks.

According to the model, there is an 80% chance New York and Louisiana will have passed their peak number of deaths by Sunday. New Jersey, Michigan, Colorado, Connecticut, Florida, Nevada and Massachusetts have an 80% chance of reaching their peak this month.

In Texas, the model shows there is a 53% chance that deaths will have peaked by May 1.

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There are several reasons predicting the impact of physical distancing is difficult. For one, some places, like Texas, mandated “stay-at-home” orders in a piecemeal fashion, often with cities and more urban counties taking the lead.

The impact of physical distancing does not become clear for three to four weeks after stay-at-home orders take affect, because of how long infected people can take to show symptoms of the virus.

All models used to predict the impact of the disease are subject to change. But the UT researchers say their model does a better job accounting for how predictive uncertainty grows the further into the future a model looks.

“While more uncertain forecasts may be disconcerting, we believe that they reflect the true range of possibilities that could unfold in the weeks ahead,” UT professor James Scott said in a press release. “Our model stands on the shoulders of the IHME model, but it corrects critical statistical flaws that led the IHME model to make many projections that, in retrospect, have turned out to be far too optimistic.”

Got a tip? Email Mose Buchele at Follow him on Twitter @mosebuchele

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Mose Buchele focuses on energy and environmental reporting at KUT. Got a tip? Email him at Follow him on Twitter @mosebuchele.
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