Updated: Model Forecasts When States, Cities Likely to See Peak in COVID-19 Deaths

A University of Texas at Austin model that projects COVID-19 deaths for all 50 U.S. states and dozens of metro areas using geolocation data from cellphones to determine the impact of social distancing within each place finds, in many communities, deaths have likely not yet peaked. The model, originally launched with state data, was updated on April 24 to be the first publicly available model to show projections of deaths also by metro area.

UT Austin researchers released the model to provide the public and decision makers alike a way to see the daily estimate of fatalities from COVID-19 in different parts of the United States for the coming weeks.

The UT model has several similarities with a model developed by the University of Washington's Institute for Health Metrics and Evaluation (IHME), but it stands in contrast because it also makes data available at the metro-area level.

The team also provided the new model to the Centers for Disease Control and Prevention and the White House Coronavirus Task Force. The model was made available to the public and decision makers in advance of scientific peer review due to the time-sensitive nature of the subject. [For detailed technical information please view the Report: UT COVID-19 Mortality Forecasting Model.]

The model was developed by several UT Austin researchers: professor James Scott and professor Lauren Ancel Meyers, who leads the UT Austin COVID-19 Modeling Consortium; graduate students Spencer Woody and Mauricio Garcia Tec; research associate Spencer Fox; and professor Kelly Gaither and Maytal Dahan, both with UT Austin's Texas Advanced Computing Center. Professor Michael Lachmann of the Santa Fe Institute also contributed to the work.

"On average, there seems to be a 3-4 week lag between when someone gets infected with COVID-19 and when they're at risk of death," Meyers said in April when the model was first released. "That means we're just beginning to see the life-saving benefits—and the clear signal in the data—of social distancing that began in mid- to late March."

The UT model tracks the local impact of social distancing through GPS traces from tens of millions of mobile phones. The cellphone data, aggregated and anonymized, reflect how often people congregate in public places such as restaurants, bars, schools, parks, pharmacies and grocery stores, as well as the amount of time people spend at work versus at home. The researchers found clear evidence that the timing and extent of people's distancing behavior differ from place to place—and that these differences matter in terms of "flattening the curve." In Texas, for example, many large cities issued stay-at-home orders that began to curtail mobility many days before a statewide policy was enacted.

Using local movement data from individual U.S. states and communities to make projections, the model differs from others that drew primarily on patterns observed in other countries. Another difference in the UT researchers' model is that it accounts for greater uncertainty further in the future.

By accounting for more sources of uncertainty, the UT model projects a greater range of plausible COVID-19 deaths than other models, like the IHME model. 

"While more uncertain forecasts may be disconcerting, we believe that they reflect the true range of possibilities that could unfold in the weeks ahead," Scott said. "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."

The research from UT COVID-19 Modeling Consortium and the Texas Advanced Computing Center informs an interactive resource found on the new website: https://covid-19.tacc.utexas.edu/projections/

​This post was updated on May 5. Find the April 17 news release here on the UT News site.