Research Briefs

UF team receives CDC grant to improve modeling of infectious disease outbreaks

Sept. 22, 2022: New funding will help close gaps in infectious disease outbreak modeling.

UF team receives CDC grant to improve modeling of infectious disease outbreaks

The team will develop a modeling framework to understand transmission dynamics of antimicrobial-resistant and antimicrobial-susceptible pathogens, such as Methicillin-resistant Staphylococcus aureus (MRSA). CDC/Melissa Dankel. Photo by James Gathany

A University of Florida team has received nearly $900,000 in funding from the Centers for Disease Control and Prevention to address current shortcomings in modeling outbreaks of emerging infectious diseases.

The team of researchers from the UF College of Public Health and Health Professions and the UF College of Medicine will develop a rigorous outbreak surveillance and detection system that could help public health officials contain outbreaks at their source, avoiding widespread health and economic impacts.

“Infectious disease surveillance and proper modeling of disease transmission are challenging tasks. To develop effective analytic tools, this project assembled an experienced team of biostatisticians and epidemiologists with complementary expertise in disease transmissibility estimation, disease surveillance, vaccine efficacies, and risk assessment and modeling,” said Peihua Qiu, Ph.D., one of the project leads and dean’s professor and chair of the department of biostatistics in the UF College of Public Health and Health Professions and the UF College of Medicine.

Identifying determinants for transmissibility, intervention effectiveness and health disparities during previous outbreaks has been hampered by a lack of data on individual exposure and clusters in the community. It is crucial to tease out the contributions of transmissions in hospitals and nursing homes and those in communities to overall spread using models that synthesize multiple data streams, the researchers said.

The UF team will develop multiple tools, including:

  • A machine-learning-based surveillance algorithm for early detection of disease clusters that updates its learned objectives using up-to-date data in real-time.
  • A competing risks modeling framework to understand transmission dynamics of antimicrobial-resistant and antimicrobial-susceptible pathogens at the individual level in health care centers and at the population level in communities.
  • An agent-based model to assess the effectiveness of strategies combining early detection, interventions and patient management for containing antimicrobial-sensitive and antimicrobial-resistant pathogens.

In addition to Qiu, the research team includes fellow principal investigator Yang Yang, Ph.D., a professor of statistics at the University of Georgia; Song Liang, Ph.D., an associate professor of environmental and global at UF PHHP; and Ira Longini, Ph.D., a UF professor of biostatistics. Both Liang and Longini are also members of UF’s Emerging Pathogens Institute.


Written by Jill Pease, originally published by the UF College of Public Health and Health Professions and republished with permission.