Climate change and microbiological food safety management: the role of in-silico tools

The link between climate change and public health risks due to pathogenic microorganisms has now been established, but it remains crucial to put in place food safety measures to mitigate and control these risks. The objective of this Perspective paper is to present in-silico tools that can facilitate microbiological food safety management in the context of climate change. The tools have been illustrated with recent studies to make their applications more tangible. The first tool, Multi-Criteria Decision Analysis (MCDA), supports the comparison and ranking of management options according to multiple criteria, including for instance microbiological risk mitigation, environmental impact and stakeholder acceptability. The second, predictive microbiology (PM), uses mathematical models to simulate microbial behaviour under environmental and process conditions and can support scenario analysis. The third, machine learning (ML), uses data-driven algorithms to identify patterns in large datasets and support prediction and classification. Although their underlying methodologies are established to varying extents, their climate-related applications in microbiological food safety remain predominantly research-based. The MCDA, PM and ML tools perform distinct but complementary functions and can contribute to decision support at different stages of microbiological food safety management and be adopted by different stakeholders. Nevertheless, they differ in terms of methodological complexity, data requirements and degree of operational implementation. Whatever the tool, its added value depends on the quality and representativeness of the available data, model validity, appropriate consideration of uncertainty, and the ability of users to interpret the outputs within their intended context.