What question did this study set out to answer?

The aim is to develop predictive risk models for vibriosis infections considering climate change.

March 13, 2026

Predictive intelligence for future vibriosis risk in the eastern United States employing Bayesian spatial modeling.

BMBailey MagersCity College of New York SKSunil KumarUniversity of Florida KBKyle D. BrumfieldUniversity of Maryland, College Park

Key Points

The aim is to develop predictive risk models for vibriosis infections considering climate change.
Bayesian spatial modeling techniques used to analyze vibriosis risk.
Assessment of risk with a 1-month lead time for infections.
Evaluation of different climate change scenarios on vibriosis risk.
Predictive models indicate varying levels of vibriosis risk based on climate conditions.
Models provide a 1-month lead time, enabling timely interventions.
Potential development of early warning systems for public health mitigation.

Abstract

This study provides predictive risk models for vibriosis infections along the eastern United States with a 1-month lead time and demonstrates risk of vibriosis under different climate change scenarios. These efforts may lead to the development of future early warning systems, allowing for mitigation of infections and benefit to public health.

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