CDC Unveils Forecasting, Outbreak Analytics Center

The Centers for Disease Control and Prevention (CDC) has launched the Center for Forecasting and Outbreak Analytics (CFA), which aims to improve outbreak and public health threat response by using infectious disease modeling and analytics to enable timely, effective decision making by leaders at federal, state, and local levels.

CFA also plans to develop a program for providing infectious disease event insights to the public to help inform individual decision making.

“I am excited we have launched CDC’s Center for Forecasting and Outbreak Analytics,” said CDC Director Rochelle P. Walensky, MD, in the press release. “This new center is an example of how we are modernizing the ways we prepare for and respond to public health threats. I am proud of the work that has come out of this group thus far and eager to see continued innovation in the use of data, modeling, and analytics to improve outbreak responses.”

CFA’s efforts are organized around three pillars: to predict, inform, and innovate. Currently, CFA is working to build an outbreak analytics team consisting of experts across multiple disciplines to improve development of data for trend prediction and emergency decision-making guidance. CFA is also onboarding communication experts to aid information sharing across public health partners and the public.

“The capabilities and team we are building at the new center will improve decision-making in a health crisis,” said Dylan George, PhD, director for operations at CFA, in the press release. “I am proud of the CFA team and excited for the future. Better data and analytics will give us better responses to protect all Americans.”

The CDC began planning to create CFA in August 2021 with $200 million of initial funding coming from the American Rescue Plan Act. In addition, the agency has awarded $26 million to federal partners and academic institutions to improve modeling and forecasting methodologies.

Using data analytics for population health, including modeling and forecasting approaches, can reveal valuable information for healthcare professionals. This was particularly helpful during the COVID-19 pandemic.

A study published in Public Health Nursing shows the relationship between high social vulnerability and COVID-19 mortality. The study’s aim was to identify and categorize social vulnerability indicators, of which household and community composition, minority status, socioeconomic status, and public health infrastructures were the most frequently cited variables affecting COVID-19. Upon review of the variables, the researchers found that groups characterized as having high social vulnerability, including minority groups, children and the elderly, and those with a lower socioeconomic status, experienced higher mortality rates from COVID-19.

Population health-based data analytics can also shine a light on gaps in care for mental health conditions.

One meta-analysis published in PLoS Medicine shows mental health treatment disparities for patients with major depressive disorder worldwide, particularly those who live in low- and lower-middle-income countries. The research indicates that the use of mental health services by individuals with major depressive disorder is 33 percent in high-income countries compared to 8 percent in low- and lower-middle-income countries, but more research is needed to address these differences.