A machine learning approach: Seasonal impact of climate change on Vibriocontamination onfood productsin the Dutch market (KB-46-005-014)

The project aims to develop a data-driven model as a climate adaptation solution to study the impact of climate change on Vibrio contamination in Dutch food products. With an innovative data science approach, we address climate and microbiological Dutch monitoring data and integrate climate change concerns into existing plans and monitoring programmes. This approach contributes to evidence-based climate adaptation solutions for the food safety authority and food business operators to adapt and integrate climate change concerns into the current food safety monitoring program. The outcome can be used as input for policy-making related to human health risks associated with Vibrio contamination.