AUSTIN, Texas – Using cellphone mobility data and COVID-19 hospital admissions data, researchers at the University of Texas at Austin reliably predicted hospital demands regions for almost two years, according to a new study published in the Proceedings of the National Academy of Sciences. The forecasting system, which city officials credit Austin with helping Austin maintain the lowest COVID-19 death rate of any major city in Texas, was designed for use by 22 municipal areas in Texas and can be used by any city to guide COVID-19 responses as the virus continues to spread.
The science team — working with elected Austin city officials, public health officials, and health system leaders — developed a powerful forecasting model and two public-facing dashboards that enabled city leaders to manage health care resources, ensure sufficient hospital capacity, and communicate pandemic risks to the public.
When the model was developed during the early months of the pandemic, it stood out among other forecasts available online. For example, the UT model incorporated detailed data on public movements and hospital admissions long before the well-known model from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. The model also provides city-level rather than state-level forecasts that are essential for anticipating and managing COVID-19-related health care surges. To do this, it includes detailed information on the age and health risks of local residents.
Forecast dashboards developed by the UT COVID-19 Modeling Consortium use intuitive graphs and spaghetti lines of hurricane forecasting to communicate immediate and future risks from COVID-19. Online daily forecasts have been helping Austin residents and local authorities make life-saving decisions since the spring of 2020. The model can be adapted to project COVID-19 health care needs in any U.S. city three weeks in advance. It uses anonymized mobile phone mobility data from SafeGraph, which shows how long people stay at home and how often they visit points of interest such as bars, restaurants and schools. This data reflects how behaviors are changing daily in response to changing COVID-19 conditions.
“Community movement data helps us assess changing COVID-19 transmission risks and anticipate health care surges weeks in advance,” said Spencer Fox, corresponding author and associate director of the UT COVID-19 Modeling Consortium.
The team also measured the relationship between mobility and COVID-19 transmission and found that precautionary measures, such as face masks and social distancing, reduced transmission risks when people were out in public.
“The relationship between mobility and COVID-19 transmission was weaker in February 2021 than in March 2020, suggesting the community found safer ways to interact in public,” Fox said.
The researchers found a strong correlation between COVID-19 policies and pandemic trends. When behaviors slackened, the virus picked up speed. When restrictions were enacted to curb surges, they had the desired effect. For example, Texas’ spring 2020 rapid reopening policy corresponded to a major surge of COVID-19 in Austin, which subsided after local restrictions and a nationwide mask mandate were implemented. the state.
“This forecasting system has helped save lives by allowing our community to see what they need to do and when they need to do it,” said Austin Mayor Steve Adler. “This tool sits at the intersection of science and public policy and shows the good that can be achieved when they are aligned. It was an honor to be part of such a brilliant team that developed and applied this system,” said Adler.
The forecasting system was developed by a municipal COVID-19 task force in Austin that included scientists, civic leaders, public health officials and health executives. Model projections have informed policy decisions and response actions throughout the pandemic, including resource planning by local hospitals, requests for additional resources from state and federal agencies, site launches and decommissioning of alternative care to provide additional health care capacity and changes in the Austin Area COVID-19 Alert Stage to communicate and manage risk. The model’s projections have been frequently discussed in public forums and highlighted by the media.
Data-driven policies and effective health messages have helped Austin maintain its lowest COVID-19 death rate of any major city in Texas.
“Our forecasts have helped the City of Austin make key decisions and communicate risk throughout the COVID-19 pandemic. City leaders trusted science and local health systems worked tirelessly to provide critical data,” said Lauren Ancel Meyers, consortium director, study co-author and professor of integrative biology. and statistics and data. science at UT Austin. “Together, we were able to build an easy-to-interpret forecasting system that can be used by any city to guide responses to COVID-19 as the virus continues to spread in cities across the United States,” said said Meyers.
Co-first authors are Spencer J. Fox of the University of Texas at Austin and Michael Lachmann of the Santa Fe Institute. The other authors are Mauricio Tec, Rémy Pasco, Spencer Woody, Tanvi Ingle, Emily Javan, S. Claiborne Johnston, James Scott and Xutong Wang from the University of Texas at Austin; Maytal Dahan and Kelly Gaither of the Texas Advanced Computing Center at UT Austin; Zhanwei Du, formerly of Meyers’ lab; The University of Hong Kong; Mark E. Escot of UT Dell Medical School and the City of Austin; and Steve Adler of the City of Austin. Meyers holds the Cooley Centennial Professorship of Integrative Biology and Statistics and Data Sciences at the University of Texas at Austin.
The research was supported by the Centers for Disease Control and Prevention, the National Institutes of Health and a donation from Tito’s Handmade Vodka. The Texas Advanced Computing Center at the University of Texas at Austin provided high-performance computing, visualization, database, and grid resources that contributed to the reported search results.
Proceedings of the National Academy of Sciences
The title of the article
Pandemic monitoring in real time using hospital admissions and mobility data
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