Climate Change and Foodborne Pathogens: A Comprehensive Review of Emerging Risks and Predictive Modelling

Background: Climate change is increasingly recognised as a driver of foodborne pathogen emergence, range expansion, and toxin production, yet no up-to-date systematic synthesis has critically appraised both observed risks and predictive modelling evidence. Aim: This systematic review evaluates the strength of evidence linking specific climate variables to emerging foodborne pathogen risks and assesses the performance and projections of current predictive modelling approaches. Methods: In accordance with PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, Embase, CAB Abstracts, and related sources were searched for studies that were published in the last 5 years. Eligible studies were majorly original research papers that combined documented emerging risks with quantitative predictive modelling. After screening 18,869 records, fifteen high-quality studies (2020–2025) meeting strict criteria for methodological rigour, global representation, and dual focus on observed change and future projection were included. Findings were synthesised narratively and presented descriptively and in tabular form. Results: Fifteen studies covering bacterial (Vibrio, Salmonella, Campylobacter, STEC, Listeria), viral (norovirus, hepatitis A), parasitic (Cryptosporidium, Cyclospora, Giardia), and mycotoxin-producing fungal pathogens were included. We found out that there was high-certainty evidence linking marine heatwaves and sea-surface temperature rise to 300–800 km poleward expansion of pathogenic Vibrio species and a projection of 40–70 % higher seafood-associated illness by 2070–2100. We also found strong evidence that more carbon dioxide, heat, and drought can increase aflatoxin (a dangerous toxin in crops) by 70–300% by 2050–2080. These toxins are already being found for the first time in southern Europe. Moderate-certainty evidence supports temperature-driven seasonal extension of Salmonella and Campylobacter and extreme precipitation as an acute trigger for STEC, norovirus, and Cryptosporidium outbreaks. Hybrid mechanistic–machine-learning models achieved the highest forecast skill and are already supporting operational early-warning systems. Conclusion: Climate change is making foodborne diseases more common around the world. The biggest concerns are toxins in crops (mycotoxins) and Vibrio bacteria in water bodies. Current prediction tools are helpful, but they do not cover all regions and do not fully include ways to adapt. Urgent translation of these tools, especially in low- and middle-income countries is required to protect global food safety.