Sheldon, UMass Amherst Colleagues Receive NIH Grant to Improve Pandemic Forecasting

UMass Amherst College of Information and Computer Sciences Associate Professor Dan Sheldon, an artificial intelligence (AI) researcher, along with colleagues Nicholas Reich, a biostatistician; Andrew Lover, an infectious disease epidemiologist; and biostatistics Ph.D. student Casey Gibson received a $300,000 grant from the NIH to develop new statistical methods for their individual mechanistic Bayesian forecasting model. It's one of the models featured on the COVID-19 Forecast Hub, the FiveThirtyEight COVID-19 Forecast tracker and the CDC website.

Led by Sheldon, who addresses large-scale data-scientific challenges using massive data sets, the team will explore machine-learning approaches for pandemic scenarios.

"New methods are needed to leverage the wealth of surveillance data at fine spatial and temporal granularity, together with associated information about policy interventions and environmental conditions over space and time, to reason directly about the mechanisms to forecast and understand the transmission dynamics of SARS-CoV-2 transmission," the researchers say. "These methods must use sound statistical and epidemiological principles while being flexible and computationally efficient to provide real-time forecasts that can guide public health decision-making and response to the highly dynamic aspects of this global crisis."

According to the researchers, these new frameworks need to be developed quickly because of "the very real potential" for COVID-19 to become an endemic, and perhaps seasonal, pathogen in the U.S., causing recurrent waves of the disease.

Adapted from a release published by the UMass Amherst Office of News and Media Relations.