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New Tool to Guide Decisions on Social Distancing Uses Hospital Data and Emphasizes Protecting the Vulnerable

New Tool to Guide Decisions on Social Distancing Uses Hospital Data and Emphasizes Protecting the Vulnerable

With communities throughout the United States combating surges in COVID-19 cases and hospitalizations, researchers at The University of Texas at Austin and Northwestern University have created a framework that helps policymakers determine which data to track and when to take action to protect their communities. The model specifies a series of trigger points to help local entities know when to tighten social distancing measures to prevent hospitals from being overrun by virus patients. The method also aims to minimize the economic impact to communities by suggesting the earliest times for safely relaxing restrictions.

The framework is described in a new paper out today in the Proceedings of the National Academy of Sciences.

The United States' continued high rate of infection means lawmakers around the country need to continue to make decisions about reinstating and relaxing social-distancing measures. Using hospital data, the new model lets local leaders know when it is time to tap the brakes on reopening versus easing restrictions.

For example, in Austin, Texas, the modelers applied this framework to help city leaders decide when to toggle between five different COVID-19 alert levels. The city is now tracking the daily number of new hospital admissions, and it recently tightened measures when the data surpassed the prescribed threshold.

"We developed this framework to ensure that COVID-19 never overwhelms local health care capacity while minimizing the economic and societal costs of strict social-distancing measures," said Lauren Ancel Meyers, a co-author of the paper and the director of The University of Texas COVID-19 Modeling Consortium.

Northwestern's Daniel Duque, the first author, said that "the approach provides clear indications of when measures should be enacted and relaxed to manage risk."

There are two key components to successfully implementing the strategy -- closely monitoring data about hospitalizations for COVID-19 and ensuring communities protect those most vulnerable to the disease.

"While many cities have implemented alert levels and new policies, our research may be the first to provide clear guidance for exactly what to track (hospital admissions data) and exactly when to act (strict thresholds)," said David Morton, chair and professor of industrial engineering and management sciences at Northwestern and a co-author of the paper. "Communities need to act long before hospital surges become dangerous. Hospital admissions data give an early indication of rapid pandemic growth, and tracking that data will ensure that hospitals maintain sufficient capacity."

In recent weeks, public health officials have expressed concerns that hospitalization data has been inconsistent, as the federal government moved the data to a new portal housed within the Department of Health and Human Services.

"COVID-19 hospitalization data is vital to tracking the changing pace of the pandemic and informing good decision-making," Meyers said.

The team also determined that preventing an unmanageable surge in hospitalizations requires adherence to strict social distancing for high-risk populations, known as cocooning. For example, the researchers estimated that failing to protect vulnerable populations more than doubles resulting deaths while also doubling the number of days in lockdown to prevent overrunning hospitals.

The framework combines two mathematical models: an underlying model that predicts how the pandemic will likely spread and an optimization model that uses admissions data from Austin hospital systems. It attempts to walk a fine line of preventing economic disaster and keeping hospital systems from becoming overwhelmed. Though the researchers used Austin data, the framework can easily be used by other communities with publicly available hospital admissions data.

"This is a general framework that can be used to design multistage triggers — not just for lockdowns but for moving between phases — exactly like we have done for Austin," Morton said. "Our framework has already guided policy changes in Austin."

In addition to Meyers, Duque and Morton, Zhanwei Du and Remy Pasco at UT Austin and Bismark Singh of Friedrich-Alexander-Universität contributed to the research. The research was funded by the National Institutes of Health and the U.S. Department of Homeland Security. 


More UT COVID-19 Modeling Consortium Updates:

Risk of Infections in Schools Assessed in New Model

The University of Texas COVID-19 Modeling Consortium offers educators and parents a new framework that uses community prevalence of COVID-19 to determine the risks of the virus being introduced in any school. The framework, published in a new interactive from The New York Times, was shared in a report delivered to Texas decision makers and cited in a report on school reopenings from the National Academies of Sciences, Engineering and Medicine. It helps leaders walk through a process of determining priorities, reviewing mitigation strategies and monitoring data with clear guideposts for assessing the risk is at any given time that a student will show up to school infected. Learn more​.

Predicting Impact of Social Distancing Measures on Coronavirus Disease Hospitalizations

Researchers from the University of Texas at Austin and colleagues have developed epidemiological models that project the spread of COVID-19 and found that school closures in the spring, in and of themselves, only slightly flattened the pandemic curve. Early implementation of strict social-distancing measures, such as shelter-in-place orders, were critical to slowing spread and preventing overwhelming surges in COVID-19 hospitalizations, the researchers explain in a new paper in the journal Emerging Infectious Disease. The team's models predict how the timing and effectiveness of social distancing impact the spread of COVID-19 and the resulting levels of hospitalizations, patients in intensive care, ventilator needs and deaths for the Austin, Texas area. LEARN MORE

New Dashboard Projects COVID-19 Infections and Hospitalizations in Austin Metro Area

The University of Texas COVID-19 Modeling Consortium has developed a new model that tracks the transmission of COVID-19 in the Austin Metro Area and projects the future of COVID-19 healthcare needs. They are making the model predictions available through the new Austin COVID-19 Dashboard in an effort to help decision makers and citizens to gain basic insight into the rapidly changing risks of COVID-10 and to anticipate surges in healthcare demand. The dashboard provides daily estimates for the rate of COVID-19 spread and projects imminent increases or decreases in COVID-19 hospitalizations and ICU patients. The model incorporates epidemiological characteristics of the disease, demographic information for the Austin area, and mobility data from anonymous cell phone tracing. It can be easily adapted to track the pandemic in other communities based on local COVID-19 hospital admissions data. LEARN MORE


​This post has been updated to include information about recent developments and reports from the consortium.

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Tuesday, 11 August 2020

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