Enter Note Done
Go to previous page in this tab
Session
  • Presentation | GH33A: Early Warning Systems for Infectious Disease Based on Climate and Environmental Variability II Oral
  • Oral
  • Bookmark Icon
  • GH33A-02: Predictive intelligence for vibriosis in the eastern United States employing Bayesian spatial modelling
  • Schedule
    Notes
  • Location Icon298-299
    NOLA CC
    Set Timezone
  •  
    View Map

Generic 'disconnected' Message
Author(s):
Bailey Magers, University of Florida (First Author, Presenting Author)
Sunil Kumar, University of Florida
Kyle Brumfield, University of Maryland
Katherine Deliz Quinones, University of Florida
Rita Colwell, University of Maryland
Antarpreet Jutla, University of Florida


Global reports of Vibrio spp. infections have increased significantly over the past few decades. Here, we forecast infection risks associated with six vibrio species – V. alginolyticus, V. cholerae non-O1/non-O139, V. fluvialis, V. mimicus, V. parahaemolyticus, and V. vulnificus – along the United States eastern seaboard. Using reported vibriosis cases from 1997 to 2019, we retrospectively modeled infection risk. Predicted probability of infections also increased throughout the study period, with annual probability of infections more than doubling between 1997 and 2019 for total vibriosis. If linear trends of environmental change persist, total vibriosis predicted probability is projected to be near 100 % in many counties by 2050 and nearly all counties by 2100 during peak infection season. Validation of models using Florida vibriosis data for 2020-2024 indicates precision may continue to improve, with model performance surpassing the calibration period, particularly during months impacted by hurricanes, as observed during previous hurricanes Helene and Milton. The demonstrated potential of these models warrants development of an early warning system for the eastern US to help mitigate the impacts of vibriosis, especially as climate change threatens to increase the risk of infections.



Scientific Discipline
Neighborhood
Type
Main Session
Discussion