The slide’s bold statement was prescient.
Marathe, a professor of computer science in UVA’s School of Engineering and Applied Science and distinguished professor at UVA’s Biocomplexity Institute, and the team of multi-disciplinary scientists representing 14 U.S. institutions and 20 international organizations were focused on developing new computational tools to help policymakers, health care providers and everyday citizens handle epidemics – keeping diseases from reaching pandemic proportions.
The presentation of their project on this snowy day was the final phase of a very long journey to win a coveted NSF Expeditions in Computing grant – a highly competitive, five-year, $10 million award aimed at propelling science in the quest to answer big societal questions.
Since the early 1900s, most of the framework for modeling how infectious diseases spread was based on differential equations developed by 1902 Nobel Prize winner Ronald Ross, a British scientist who used math to study malaria. Ross’ method came out of the worlds of physics and chemistry, where molecules interact with each other and the reaction creates new molecules. In some ways, scientists believed, this simulated the way humans interact and possibly spread disease.
That early approach provided basic estimates and only required the use of a pencil and paper. However, it didn’t, and couldn’t, account for the complexities of human-to-human and human-to-animal interactions and the detailed interactions people have every day, referred to by today’s scientists as multi-scale, multi-layer networks. These interactions have grown by orders of magnitude since Ross worked out his first equation, as populations have increased exponentially, global travel has become routine, and diseases have emerged that are resistant to treatments.
Today, Marathe and other computer science engineers and scientists weave together high-performance computing, machine learning and artificial intelligence to run complex simulations that incorporate many different kinds of datasets, like activity data that show how people move over time, alongside fitness-tracking data, health-related data, weather data and census data, to name a few.
“Our hypothesis was that the structural features of the social contact network – i.e., patterns of interactions between individuals [referred to as nodes] in a social network – have a tremendous impact on the outcome of epidemics and that they should be represented as faithfully as they can,” Marathe said. “This had become a computer science question, and now computer science was mature enough to undertake it.”
In the years prior to their NSF presentation, Marathe and his colleagues at the Biocomplexity Institute had many successes using computer science to model the spread of infectious diseases, but there were still a lot of questions. They had much research to do in applying computational modeling to real-time epidemiology – research they hope will one day give people more reliable information than ever before to develop intervention plans before infectious diseases reach pandemic proportions.
Sitting at the conference table on that wintry, December day, Marathe knew they had assembled a brilliant cohort of researchers eager to earn the NSF’s support for their very important, complex work, and that the chances were pretty good they might get the grant.
Still, there was one critical thing that was totally out of their control, although fundamental to the project: To do real-time epidemiology, they would need to study an infectious disease somewhere in the world during the five-year grant.
Infectious diseases cause more than 13 million deaths per year worldwide. Rapid growth in the human population and its ability to adapt to a variety of environmental conditions has resulted in unprecedented levels of interaction between humans and other species. This rise in interaction combined with emerging trends in globalization, anti-microbial resistance, urbanization, climate change, and ecological pressures, has increased the risk of a global pandemic.
Computation and data sciences can capture the complexities underlying these disease determinants and revolutionize real-time epidemiology – leading to fundamentally new ways to reduce the global burden of infectious diseases that has plagued humanity for thousands of years.
– Excerpt from the abstract submitted to the NSF by Marathe and the research team, written in 2019
As the program director for the NSF’s Expeditions in Computing grant since 2009, Mitra Basu has read and evaluated hundreds of well-conceived proposals spanning a range of computer science disciplines focused on huge societal problems. In that time, however, her group has awarded only about 25 grants.
Research teams spend years building up the expertise needed to earn an Expeditions grant.
The basis of Marathe’s team’s proposal to NSF can be traced back two decades to the Los Alamos National Laboratory. There, Marathe worked with colleagues Christopher L. Barrett, Stephen Eubank and Anil Vullikanti in devising a novel way to study an epidemic science problem. Part of this work was funded through the National Institutes of Health’s Models of Infectious Disease Agent Study project for 13 years. Barrett is now the UVA Biocomplexity Institute’s executive director, a distinguished professor of biocomplexity and a professor of computer science at UVA Engineering. Eubank is the institute’s deputy director in the Network Systems Science and Advanced Computing Division and a professor in the UVA School of Medicine’s Department of Public Health Sciences.
Initially, the group developed models using high-performance computers to study infrastructure, transportation and communications problems for the Department of Defense and other governmental organizations, and they wanted to apply their research to human health, specifically infectious disease spread.