Predictive intelligence for future vibriosis risk in the eastern United States employing Bayesian spatial modeling
Bailey M Magers1, Sunil Kumar1, Kyle D Brumfield2,3,4
1Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.
Applied and Environmental Microbiology
|March 11, 2026
Summary
Vibriosis infection risk along the US east coast is increasing. New Bayesian models predict vibriosis outbreaks one month in advance, crucial for public health.
Area of Science:
- Environmental science and public health
- Infectious disease epidemiology
- Spatial statistics and modeling
Background:
- Vibriosis infections have risen significantly in recent decades.
- Environmental factors drive Vibrio spp. proliferation, necessitating predictive models.
- Previous studies lacked robust spatial-temporal forecasting for multiple Vibrio species.
Purpose of the Study:
- To develop and implement Bayesian spatial models (INLA-SPDE) for forecasting Vibrio spp. infection risks.
- To assess the impact of environmental and socioeconomic factors on vibriosis risk.
- To project future vibriosis risk under various climate change scenarios and inform early warning systems.
Main Methods:
- Utilized Bayesian spatial modeling (INLA-SPDE) with 1997-2019 vibriosis data from the US eastern seaboard.
- Incorporated 11 environmental and 4 socioeconomic predictors to model infection risk.
- Integrated Bio-ORACLE v3.0 (CMIP6) data for future climate change scenario projections.
Main Results:
- The total vibriosis model demonstrated increasing precision over time (0.71% per year).
- Predicted vibriosis probability nearly doubled annually from 1997-2019 (93.4% increase).
- Models showed high precision during hurricane seasons (65.0%) and projected near-universal risk by century's end under likely scenarios.
Conclusions:
- Predictive models for vibriosis risk show significant promise for public health.
- An early warning system for vibriosis in the eastern US is critical given rising threats.
- Forecasting vibriosis risk under climate change is essential for proactive mitigation strategies.
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