Few people think of flu season as much more than sniffles and sleepless nights. For SFI External Professor Lauren Ancel Meyers, it’s a chance to study how human epidemics develop -- and try to head them off.
Working with the Texas Department of State Health Services and a team of University of Texas researchers, Meyers led the development of the Texas Pandemic Flu Toolkit, a web-based service that simulates the spread of pandemic flu through the state, forecasts the number of flu hospitalizations, and determines where and when to place ventilators to minimize fatalities.
The toolkit can be used in emergency situations for real-time decision-making. Public health officials might use the forecaster tool to determine when a pandemic might peak and what kind of magnitude they might see in terms of infections and hospitalizations. It might also be used to develop scenarios of probable pandemics and to see how they may impact different locations, age groups, and demographics. Various interventions, such as antivirals, vaccines, and public health announcements, can be input into the forecasts to determine their effect at different stages in the pandemic's evolution.
Read the article in Physorg (June 7, 2012)
Read the article in the SFI Update (March-April 2012)
Watch Meyers describe the toolkit (SFI video presentation, 57 minutes)
“The spread and control of infectious diseases in human populations is an enormously complex system, driven by non-trivial interactions between continually evolving pathogens, diverse host immune systems, and individual and organizational decision-making,” says Meyers.
In 2009 she helped track the emerging H1N1 pandemic, and worked with the CDC and other public health agencies to mathematically model the virus’s movement through the population.
“Understanding the dynamics of human contact networks and health-related behavior is critical to making good predictions and designing effective interventions,” she says.
Meyers has been developing an approach called contact network epidemiology. In her models, individuals or susceptible populations are represented by nodes, which are connected by edges that represent contacts that can lead to disease transmission. The network models can account for varying social behaviors and varying levels of vulnerability, and can even help reveal the likely efficacies of intervention strategies such as vaccinations, quarantines, and distributing antiviral medications.
“We’re learning a lot about infectious diseases from the growing volumes of data produced by surveillance systems and high throughput laboratory methods,” Meyers says. “Innovative modeling techniques have become indispensable to this interdisciplinary field, as we seek to advance in our understanding of epidemics and improve public health.”