IVAC’s Dr. Shaun Truelove to Co-Lead Infectious Disease Modeling and Analytics Center
The Centers for Disease Control and Prevention’s Center for Forecasting and Outbreak Analytics recently established the Outbreak Analytics and Disease Modeling Network, a national network comprised of 13 centers around the country. Dr. Shaun Truelove, IVAC faculty and an assistant scientist at the Johns Hopkins Bloomberg School of Public Health, will be co-leading one of these centers, the Atlantic Coast Center for Infectious Disease Dynamics and Analytics (ACCIDDA), alongside colleagues from the University of North Carolina-Chapel Hill.
ACCIDDA will leverage the diverse expertise and experience of faculty from across Johns Hopkins University, including the Bloomberg School of Public Health, the School of Medicine, and the Whiting School of Engineering. Collaborators include individuals from the University of North Carolina-Chapel Hill, the Johns Hopkins Applied Physics Laboratory, the University of Florida, and the University of Pittsburgh. In addition to creating new modeling tools and establishing new data resources for disease surveillance, ACCIDDA will also serve as the OADM Network Coordinator.
ACCIDDA builds on the accomplishments of the team, including creating and running the COVID-19 Scenario Modeling Hub, JHU’s COVID-19 Map, JHU’s Novel Coronavirus Research Compendium, and numerous other research and response efforts across these institutions, which have been instrumental in guiding policy related to COVID-19. The team will continue modeling for COVID-19, influenza, and respiratory syncytial virus (RSV) and also plan to explore vector-borne diseases such as Zika, dengue, and chikungunya. “The general goal is to build tools that will make us more prepared in the future,” Dr. Truelove explained. “We don’t know what the next major crisis will be, but the idea is to build technologies that can be used for these different types of pathogens, or any new pathogen that comes along.” Although this work will have a domestic focus, these tools should also be generalizable to the broader global population.
As many public health decisions about outbreak response are made at the state, county, or local level, these tools will use localized data along with AI and machine-learning approaches to translate higher-level estimates, such as forecasts or scenario projections, to a more local scale, providing public health departments with more accurate and usable data at levels where critical response decisions are being made. This center also aims to develop programs to increase training and expertise in disease dynamics and analytics for direct public health engagement, ensuring that there are more individuals working within academic institutions and public health organizations with the expertise needed to respond to disease outbreaks.
With the goal of increased expertise and capacity to impact public health, ACCIDDA will be working directly with several public health partners, including the Maryland Department of Health, the North Carolina Department of Health and Human Services, the California Department of Public Health, the Baltimore City Health Department, and the Navajo Epidemiology Center.