Investigating foodborne pathogen outbreaks: an integrated framework for tracing, detection, and risk assessment in food systems

Abstract

Foodborne pathogen outbreaks impose a substantial and escalating burden on global public health, food systems, and economies, with the World Health Organization estimating over 600 million illness episodes and 420,000 deaths annually. Effective outbreak investigation requires harmonizing microbiological detection, molecular source tracing, and quantitative risk assessment within a single, coherent analytical architecture—a capacity that current fragmented approaches consistently fail to deliver. This review presents a novel, food-system-centered integrated framework for foodborne pathogen outbreak investigation that, for the first time, explicitly unifies conventional microbiology, molecular and whole-genome sequencing (WGS)-based typing, foodomics (metagenomics, proteomics, metabolomics), artificial intelligence and machine learning (AI/ML)-driven source prediction, geographic information systems (GIS)-based spatial epidemiology, and iterative quantitative microbial risk assessment (QMRA) within a single investigative architecture. The framework is further differentiated by a three-tiered adaptive implementation model designed explicitly for resource-limited settings and by dedicated protocols for informal food supply chains—two critical gaps absent from existing WHO/FAO and CDC/EFSA guidelines. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science (1997–2025), with emphasis on evidence published between 2021 and 2025. The framework addresses three structural limitations of current practice: investigative fragmentation, under-integration of risk assessment, and inapplicability in low- and middle-income country (LMIC) contexts. By anchoring investigation in food and production environments rather than in clinical surveillance alone, and by embedding iterative risk assessment from the earliest investigative stage, the proposed framework supports more rapid, accurate, and equitable outbreak responses. Limitations of the review and directions for future validation research are discussed.

Keywords

foodborne pathogen outbreak / artificial intelligence / geographic information systems / source attribution / resource-limited settings / informal food supply chain

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