Could preparing for and dealing with contagious illnesses such as COVID become as streamlined as getting ready for a storm? Michael Sweat, Ph.D., thinks so. "We have disaster planning for all kinds of things, but we haven't really gotten them in place at the state level for infectious disease outbreaks."
Sweat, whose expertise at using mathematical modeling to track and predict COVID was on display through the Medical University of South Carolina's epidemiology intelligence project during the pandemic, is part of a new push to use data to spot disease outbreaks early and respond to them in the most effective and efficient ways possible.
It will begin by looking to the past. "We're going to go back and focus on waves of RSV, COVID and influenza. We'll do a very complex analysis of how they unfolded and what the trigger points are that make you become aware that you've got a problem and need to act," Sweat said.
Then, he and colleagues across the state will take what they learn and develop systems that could keep some illnesses from taking root.
The project is led by Lior Rennert, Ph.D., director of the Center for Public Health Modeling and Response at Clemson University. Rennert and his team secured a $17.5 million grant from the Centers for Disease Control and Prevention. It's called the Disease Modeling and Analytics to Inform Outbreak Preparedness, Response, Intervention, Mitigation and Elimination in South Carolina initiative, or DMA-PRIME.
MUSC is among several partners in the state taking part. The others are:
- Clemson Rural Health.
- Prisma Health.
- University of South Carolina.
- South Carolina Department of Health and Environmental Control.
- South Carolina Emergency Management Division.
Sweat worked with Rennert and other scientists, data analysts and infectious disease experts to put together the grant request. Sweat has a solid background for the effort. He's not only a professor in the College of Medicine at MUSC and an adjunct professor at the Johns Hopkins Bloomberg School of Public Health but also a former research scientist with the CDC and former mathematical modeler for Family Health International.
Sweat looks forward to putting that experience to use as he and other specialists at MUSC collaborate with colleagues across the state to explore new avenues for keeping the public as safe as possible from infectious diseases.
The group will start by developing a protocol for the project, which will include the use of:
- Electronic health records from hospitals, which will not include patients' identity.
- Wastewater samples from many more locations than were available during the COVID pandemic.
- Digital traces - in other words, what people are searching for online. A lot of searches for runny nose and fever in one area, for example, may signal a brewing outbreak of illness.
That will allow the experts to forecast when and where an outbreak might happen. "This analysis would feed into a system that would be in place already. If we have another big flu outbreak, or another coronavirus comes up, there would be plans in place, information systems to feed leaders guidelines about what activities could be taken and feed the data down to the local level," Sweat said.
It might also prevent some of the problems that COVID caused. "The health systems have big concerns about two big things. Staffing was a big issue. And also, commodities. When COVID hit, it disrupted supply chains. We didn't have stocks of masks that we would've wanted to have and things like that. So this toolkit's meant to give some advanced warning and some analyses about what you might need to have in place."
The CDC is funding the project for five years. It's among about a dozen initiatives the agency is funding to initiate work with the CDC's Center for Forecasting and Outbreak Analytics. The goal is to establish the Outbreak Analytics and Disease Modeling Network. It's a welcome development, Sweat said.
"The CDC was criticized pretty heavily in the COVID time. Many recommendations were made to strengthen the nation's epidemic modeling capacity. And so they have put a lot of money into developing this new group related to epidemic forecasting."
That forecasting will take into consideration the fact that the potential outbreaks of the future may be unlike the diseases seen before. "The pathogens that might pop up could be completely novel. So it's helpful to have a model in place. You can adjust that model when you start getting information with, say, a new novel coronavirus," Sweat said.
"You'd rely on what you know from the past, and then you'd have to update that as you go. And that's part of what I think CDC wants to do is build these networks that can be very quickly mobilized to do analysis in regional and local settings."
