Strengthening South Carolina's Infectious Disease Response: The DMA-PRIME Initiative

In an era when infectious diseases can spread rapidly across populations – a fact the world learned during the COVID-19 pandemic – effective preparedness and response are critical to safeguarding public health.

Researchers from the Medical University of South Carolina have paired with their counterparts from Clemson University and the University of South Carolina for a federally funded project aimed at improving the ability of communities and health organizations to quickly detect and respond to critical health threats.

Supported through a $17.5 million, seven-year grant from the Centers for Disease Control and Prevention (CDC), through its Center for Forecasting and Outbreak Analytics, the grant pairs the educational institutions with Clemson Rural Health, Prisma Health, the South Carolina Department of Health and Environmental Control and the South Carolina Emergency Management Division in the collaborative effort. Lior Rennert, Ph.D., associate professor in the public health sciences department and director of the Center for Public Health Modeling and Response at Clemson University, is the project’s principal investigator.

Coined Disease Modeling and Analytics to Inform Outbreak Preparedness, Response, Intervention, Mitigation and Elimination (DMA-PRIME), the initiative seeks to enhance real-time outbreak forecasting, data-driven decision-making and communication across South Carolina, with the long-term goal of saving lives and improving health outcomes for residents.

A multi-faceted approach to outbreak management

DMA-PRIME is an effort to bolster South Carolina’s ability to detect, prevent, prepare for and respond to infectious disease outbreaks, including the procurement of critical data and their integration into cutting-edge forecasting and analytics tools. These tools are designed to provide public health officials, and the public, with actionable insights into emerging threats, enabling timely interventions.

More specifically, the project’s three key components include:

1. Data integration and analytics tools. DMA-PRIME will gather essential data from various sources, integrating it into proven infectious disease forecasting models and outbreak analytics. These tools will help predict the trajectory of an outbreak, identify high-risk areas and allocate resources efficiently.
2. Decision-support toolkits for public health. By integrating the analytics tools into practical decision-support toolkits, the initiative will equip health professionals with the information needed to make informed decisions on interventions such as testing, treatment, vaccination and resource allocation and allow heath systems to prepare for emerging infectious disease threats.
3. Visualization and communication. An essential part of the initiative is improving how data is visualized and communicated to decision-makers and the public. Enhanced data visualization techniques will ensure that health officials and communities alike can quickly grasp complex information and take appropriate action to protect public health.

MUSC Professor and Faculty Director of the Center for Global Health Michael D. Sweat, Ph.D., the lead investigator at MUSC, said researchers are using artificial intelligence and machine learning to monitor de-identified electronic data, both public and private from the health systems, to review historical and ongoing medical data involving COVID, influenza and respiratory syncytial virus (RSV). 

“We can use these very advanced mathematical techniques to ask, ‘What are the correlates of when we see outbreaks; who does it target? What are the comorbidities? Who got hospitalized? Where did they live? How long were they in the hospital for?’” Sweat said. “Then, using all these data, they geolocate that so they're able to make projections. It really is remarkable.”

Sweat remarked on the incredible ability that machine learning provides in scanning hundreds of thousands of medical records that have been blinded – where all personally identifying information is removed – including detailed symptoms that are manually entered into a patient’s report by the practitioner. When similar symptoms are all presenting in a particular location, outbreaks can be more quickly identified and efforts made at mitigation before wider community spread occurs, he said.

“Say we had an outbreak of H5N1, which is bird flu, and it was spreading rapidly,” he said. “The models are being designed so that they can zone into the area at the zip code level and say, ‘Here’s what the current infection rate is, but we project within three weeks it’s going to look like this.’”

Sweat said the modeling that has occurred to date using past data already has a success rate of greater than 80 percent at predicting outcomes, which at this early of a stage of development, is promising as the project teams look to the years ahead.

Long-term impact: A stronger, more resilient public health system

The DMA-PRIME initiative is not just a short-term project. It is part of a broader effort to build long-term resilience in South Carolina’s public health infrastructure. By equipping decision-makers at all levels with innovative analytic tools and better data, the expectation is that DMA-PRIME will help the state respond to infectious disease outbreaks more swiftly and effectively.

Sweat noted that there are research groups in other regions of the U.S. similarly funded by the CDC, looking into the same issue and developing their own solutions. One of the goals of the initiative is to expand its reach beyond any one state or region of the country. By improving real-time outbreak forecasting and response, DMA-PRIME holds the potential to save countless lives while improving health outcomes for communities across the state and beyond. In addition, the regional projects are designed so that if one area of the country experiences an infectious disease outbreak, the other centers will be available to assist in the response.

“With the right computational power and information,” Sweat said, “you can make fairly reasonable estimations of what's likely to happen in the event of an outbreak at least in the short term and maybe even in the longer term. It's a really exciting project to be a part of.”