News

The University of Texas at Austin's COVID-19 Modeling Consortium has launched a new online dashboard to track the spread and impact of the virus, including in hospitals across Texas, with detailed information for 22 areas.

Patients with undiagnosed flu symptoms who actually had COVID-19 last winter were among thousands of undetected early cases of the disease at the beginning of this year. In a new paper in The Lancet's open-access journal EClinicalMedicine, epidemiological researchers from The University of Texas at Austin estimated COVID-19 to be far more widespread in Wuhan, China, and Seattle, Washington, weeks ahead of lockdown measures in each city.

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.

In response to high demand for professionals with scientific and technical training to understand and work with massive amounts of data, The University of Texas at Austin is set to launch a new online master's degree program in data science. Pending final approval by UT System and the Texas Higher Education Coordinating Board, the new program will be a collaboration between the Department of Computer Science, ranked among the top 10 programs in the country by U.S. News and World Report; the Department of Statistics and Data Sciences, one of the university's newest and fastest-growing departments; and online learning company edX.

A new analysis of COVID-19 outbreaks in 58 cities has found that places that took longer to begin implementing social distancing measures spent more time with the virus rapidly spreading than others that acted more quickly

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.

A new pandemic model of COVID-19 shows the positive role social distancing can play in preventing the spread of the illness in areas across the state. The report by researchers in The University of Texas at Austin's College of Natural Sciences projects significantly higher numbers of cases of infection, hospitalizations, intensive care unit admissions and deaths in 22 Texas communities under scenarios in which social distancing measures are moderate.

UPDATE: Revised model projections were released on April 6. Read the full report.

Since 2012 a pandemic-planning tool developed by researchers at The University of Texas at Austin has helped public health officials plan for the consequences of a deadly and virulent virus. Now the pandemic modeler who developed the toolkit is studying COVID-19 and has built a new model to project the spread of COVID-19 across the U.S. She has teamed up with Dell Medical School to assess the potential impact of the pandemic in the Austin-Round Rock area.

Infectious disease researchers at The University of Texas at Austin studying the novel coronavirus were able to identify how quickly the virus can spread, a factor that may help public health officials in their efforts at containment. They found that time between cases in a chain of transmission is less than a week and that more than 10% of patients are infected by somebody who has the virus but does not yet have symptoms.

Infectious disease researchers at The University of Texas at Austin and other institutions in Hong Kong, mainland China and France have concluded there is a high probability that the deadly Wuhan coronavirus spread beyond Wuhan and other quarantined cities before Chinese officials were able to put a quarantine in place. At least 128 cities in China outside of the quarantine zone, including cities with no reported cases to date, had a greater than even risk of exposure, according to a paper currently in press with Emerging Infectious Diseases, a journal of the U.S. Centers for Disease Control and Prevention.

Advances in machine learning are announced every day, but efforts to fundamentally rethink the core algorithms of AI are rare.

Abhra Sarkar, assistant professor of statistics and data sciences at the University of Texas at Austin, was awarded the 2018 Mitchell Prize by the International Society for Bayesian Analysis in August for his research in animal vocalization modelling.

Boosting academic success does not have to derive from new teachers or curriculum; it can also come from changing students' attitudes about their abilities through a short online intervention, according to the latest findings from the National Study of Learning Mindsets published in Nature on Aug. 7. Faculty in the Department of Statistics and Data Sciences contributed to the research.

As a new academic year approaches, we aim to introduce you to some of the scientists and mathematicians in our community, especially faculty who joined our UT community recently. First up: Corwin Zigler, associate professor in UT Austin's Department of Statistics and Data Sciences and in the Dell Medical School's Department of Women's Health.

Kate Calder, currently a professor of statistics and co-director of the Mathematical Biosciences Institute at Ohio State University, will be the new chair of UT Austin's Department of Statistics and Data Sciences (SDS), beginning in the fall. Calder, who will be on campus this month for an in-depth public conversation about computational health April 30th, is an award-winning statistician.