From Pandemic to Prevention
Learning from the past, Texas researchers used computational technology to develop a toolkit which would help the state prepare itself more effectively against future infections...
In 2009...The H1N1 Influenza A virus was first identified in the U.S. by the Centers for Disease Control and Prevention (CDC) on April 15.
- The "swine flu," as it came to be known, was traced to an epidemic in Mexico, and spread internationally until it was declared a pandemic in June by the World Health Organization (WHO).
- During the pandemic, Lauren Ancel Meyers, expert in infectious disease epidemiology at The University of Texas at Austin, worked with national and international public health experts by creating simulations of how H1N1 influenza would spread across populations.
- The models, designed by Meyers and her team using the Texas Advanced Computing Center's (TACC) Lonestar supercomputer, incorporated the latest information on H1N1 and determined the most optimal use of preventative resources to control the spread of the virus.
- The number of confirmed cases of this new strain of influenza continued to increase until October, and by the end of February 2010 the virus was estimated to have caused 15,921 deaths worldwide.
- By summer 2010, infection levels had fallen to the point where WHO declared the pandemic over, though the virus continued to infect people for years to come at much lower rates.
A recent study published by researchers from the CDC indicates that the actual death toll for the first year of infection might have been closer to 284,400 casualties worldwide.Using a new data and calculations, researchers estimated that 51 percent of deaths occurred in Africa and Southeast Asia, a dramatic increase from WHO's 12 percent estimate, and meant the number of deaths resulting from H1N1 was between 151,700 and 575,400.
- Following the 2009 pandemic, the Texas Department of Health Services contacted Meyers to continue building quantitative tools useful for decision-making during future pandemics.
Meyers worked with an interdisciplinary team, consisting of experts in mathematics, biology, statistics, engineering and computing, to develop the Texas Pandemic Flu Toolkit.
- This web-based service can simulate the spread of a pandemic across the state of Texas, based on demographic information, traffic patterns, properties of the virus and other data.The toolkit can also be used in emergency situations to optimize response efforts and resource use.
- An example of this is the Texas Pandemic Flu Forecasting tool, which predicts regional and state-wide hospitalizations based on data from various sources, especially the Outpatient Influenza-Like Illness Surveillance Network (ILINet).
- These data can then be imported into the Texas Ventilator Stockpiling tool, which computes the best solutions for central and regional stockpiles of ventilators, which are crucial for treatment, based on the expected demand.
The toolkit can also be used to simulate probable pandemic scenarios and look at the impact of a potential outbreak on different age groups, demographics or locations.
The effect of intervention via vaccines, antivirals and public health announcements can also be modeled to analyze the effect it would have on the spread of the disease.
Meyers and her team were able to build this toolkit with help from the Texas Advanced Computing Center, whose high-performance computing power was used to forecast infections and to develop visualizations that vividly depicted the spread of the disease.
The Center's resources also permitted data from the toolkit to be processed and distributed simultaneously to stakeholders whose actions or decisions could alter the impact of a new infection in the future.
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