EMERGING BIOTHREATSFighting Biological Threats

Argonne National Laboratory has developed computer models to predict how disease can spread. With funding from the U.S. Department of Energy, it will work with Sandia National Laboratories algorithms to make them better.

Modeling the emergence and spread of biological threats isn’t as routine as forecasting the weather, but scientists in two of the U.S. Department of Energy’s (DOE) national laboratories were awarded funding to try to make it so.

DOE’s Argonne National Laboratoryand Sandia National Laboratories were one of the three projects to receive a total of $5 million from DOE to advance computational tools to better prepare for natural and human-created biological threats. The laboratories will work together to harness Sandia’s algorithms of real-world outcomes to Argonne’s high performance models that address spread transmission and control of diseases.

The projects fall under the DOE Office of Science’s new Bio-preparedness Research Virtual Environment initiative, which focuses on developing scientific capabilities that aid in the prevention and response to potential biothreats.

“We want models that mimic reality,” said Jonathan Ozik, principal computational scientist at Argonne. ​“By calibrating them, we will be able to trust that outcomes from computational experiments carried out with the models have a good chance of meaningfully reflecting reality.”

Those outcomes can provide insight into the intricacies of disease transmission as well as the effectiveness of vaccination efforts. Provided to municipal and state public health officials, the outcomes could serve as potential guidelines for the development of mitigation initiatives.

The disease model Argonne developed was first used in the early 2000s when a MRSA epidemic emerged in Chicago. The same model was later applied to the threat of an Ebola outbreak in 2014.

More recently, Argonne researchers developed CityCOVID, a highly refined version of the model, to simulate the spread of COVID-19 in Chicago. By forecasting new infections, hospitalizations and deaths, the model provided a computational platform for investigating the potential impacts of nonpharmaceutical interventions for mitigating the spread of COVID-19. It was chosen as one of only four finalists for the 2020 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research, which recognized outstanding research achievement towards the understanding of the COVID-19 pandemic through the use of high performance computing (HPC).

Developing such models, known as agent-based modeling (ABM), is a complex effort and HPC becomes critical. This is due to the large amount of data the model requires, the number of parameters researchers must take into account and additional factors or inputs that combine to make the model as accurate as possible.