Principal investigator Nick Ruktanonchai, assistant professor of population health sciences, and co-investigators Corrine Ruktanonchai, postdoctoral associate, and Rachel Silverman, research scientist in epidemiology, in the Department of Population Health Sciences have been awarded a research grant from The Thomas F. and Kate Miller Jeffress Memorial Trust to investigate how machine learning and mathematical models can be used to develop an early warning system to predict COVID-19 transmission.

TITLE
Mobility-based Outbreaks in Virginia Early Response System (MOVERS): Using real-time mobility data to develop an infectious disease outbreak early warning system for Virginia

FUNDING AGENCY
The Thomas F. and Kate Miller Jeffress Memorial Trust

TOTAL AWARD
DIRECT: $100,000

DURATION OF AWARD
July 1, 2021 — June 30, 2022

KEY FACULTY PERSONNEL
PI/PD
: Nick Ruktanonchai
CO-PIs/Co-PDs: Corrine Ruktanonchai, Rachel Silverman

SIGNFICANCE
Human mobility data have transformed how we understand and predict the transmission of infectious diseases such as COVID-19. They allow us to understand where cases may be imported from, where pathogens may go next, and how various communities are interconnected in terms of intervention impact. Here, we use novel machine learning and mathematical approaches to understand how various sources of human mobility data can be used to predict COVID-19 transmission in the near-term. We will use these inferences to improve prediction of variants of concern as they spread throughout Virginia within a metapopulation model, towards providing a near-real time early warning system that can inform where and when VOCs will appear next, and where future interventions should be focused.