Which Pandemic Forecasting Model is Best?

UT Austin supercomputer powered model integrates GPS data from mobile phones and social distancing attributes
women running in a park, with masks and separate, no others around them
(Precision Vaccinations)

Given the new debate regarding which disease pandemic model generates the most accurate forecast, a new model tracks the local impact of social distancing through GPS traces from tens of millions of mobile phones.

Researchers at The University of Texas at Austin (UT Austin) announced on April 17, 2020, that they have released a model that provides a daily estimate of fatalities related to the COVID-19 disease for the coming weeks.

Furthermore, since the SARS-CoV-2 coronavirus pandemic is ‘time-sensitive’, the UT Austin team is providing the new model to the Centers for Disease Control and Prevention and the White House Coronavirus Task Force in advance of scientific peer review.

This innovative model finds that the number of fatalities almost certainly has not yet peaked and is not likely to peak in many states until after May 1, 2020.

This is a finding that is in contrast to one of the most frequently cited models.

While the UT Austin model has several similarities with the one developed by the University of Washington’s Institute for Health Metrics and Evaluation (IHME), it stands in contrast because it uses data to estimate the impact of recent social distancing measures within each state.

The new model predicts that there are 2 states, New York and Louisiana, which will be past their peak by April 19 with at least 80% certainty. 

The model also predicts that 7 others, plus Washington, D.C., have at least an 80% chance of peaking in April: New Jersey, Michigan, Colorado, Connecticut, Florida, Nevada, and Massachusetts.

The model was developed by several UT Austin researchers: professor James Scott and professor Lauren Ancel Meyers, who leads the UT COVID-19 Modeling Consortium.

“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. 

“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 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 across states—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.

In addition to using local movement data from individual U.S. states to make projections rather than drawing on patterns observed in other countries, another difference in the UT researchers’ model is that it accounts for greater uncertainty further in the future. 

Recent updates of the IHME model have resulted in large swings in the projected numbers of COVID-19 deaths. In some cities, the forecasted peaks have changed by more than a factor of 10, yet the confidence intervals in the IHME projections show near certainty regarding the number of COVID-19 deaths that will occur six weeks from now.

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

In Wisconsin, for example, the new model predicts that there could be anywhere from 0 to 89 deaths on May 1, whereas IHME predicts a maximum of 5 deaths; likewise it caps May 1 deaths at 71 for Colorado, whereas IHME projects no more than 6.

“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.”

For detailed technical information please view the report: UT COVID-19 Mortality Forecasting Model.

The research was published today by the UT COVID-19 Modeling Consortium at the Texas Advanced Computing Center, which hosts the projections on a new website.

COVID-19 modeling forecast news published by Precision Vaccinations.