Review design and scope

This study is a comprehensive narrative review with elements of structured synthesis, designed to develop and validate a novel integrated framework for foodborne pathogen outbreak investigation. The review synthesizes evidence across five interrelated domains: (1) conventional microbiological detection methods; (2) molecular and genomic typing techniques; (3) foodomics-based analytical approaches; (4) AI/ML and GIS-based investigative tools; and (5) QMRA and risk management. The scope was intentionally broad to capture the full methodological landscape, consistent with the objective of framework development rather than meta-analytic effect estimation.

Literature search strategy

A systematic literature search was conducted in three major electronic databases: PubMed/MEDLINE, Scopus, and Web of Science. The search covered publications from January 1997 to February 2025, with priority given to high-impact evidence published between 2021 and 2025 to ensure currency of the framework. Search terms were applied using Boolean operators (AND/OR) and included the following MeSH and free-text terms: “foodborne outbreak” OR “foodborne pathogen”; AND “outbreak investigation” OR “source attribution” OR “source tracing”; AND “whole-genome sequencing” OR “WGS” OR “foodomics” OR “metagenomics” OR “proteomics” OR “metabolomics”; AND “artificial intelligence” OR “machine learning” OR “deep learning”; AND “geographic information systems” OR “GIS” OR “spatial epidemiology”; AND “quantitative microbial risk assessment” OR “QMRA”; AND “food safety” OR “food supply chain” OR “food systems”. Additional targeted searches were conducted for informal food supply chains, LMIC food safety, and tiered laboratory frameworks. Reference lists of included reviews were manually screened for additional relevant citations.

Inclusion and exclusion criteria

Studies were included if they: (1) were published in peer-reviewed journals or constituted authoritative guidelines from WHO, FAO, EFSA, or CDC; (2) addressed one or more of the five framework domains defined above; (3) involved foodborne bacterial, viral, or parasitic pathogens in food matrices, food production environments, or associated supply chains; and (4) were available in English. Studies were excluded if they: (1) focused exclusively on clinical disease management without reference to food systems or investigation methodology; (2) addressed environmental pathogens not transmitted via food; or (3) were conference abstracts, editorials, or letters without primary data or substantive methodological content. No date restriction was applied to foundational methodological papers; however, for rapidly evolving areas (AI/ML, GIS, WGS), priority was given to publications from 2018 onward.

Data extraction, quality appraisal, and framework development

Relevant methodological information was extracted from included studies, including study design, pathogen(s) investigated, food matrices and supply chain contexts examined, analytical methods applied, source attribution approaches, risk assessment frameworks employed, and implementation requirements. For quantitative studies, performance metrics (sensitivity, specificity, turnaround time, cost) were extracted where reported. Quality of primary studies was assessed using a structured appraisal approach considering study design, sample size, methodological rigor, and relevance to framework components. Authoritative guidelines and systematic reviews were given highest evidential weight. Framework development followed an iterative process of evidence synthesis, expert judgment, and structured gap analysis. The initial database search identified a total of 412 records, of which 50 studies and authoritative guidelines met the inclusion criteria and were incorporated into the review. Of these, the distribution across the five framework domains was as follows: (1) conventional microbiological detection methods: 14 studies; (2) molecular and genomic typing techniques: 12 studies; (3) foodomics-based analytical approaches: 6 studies; (4) AI/ML and GIS-based investigative tools: 10 studies; and (5) QMRA and risk management: 8 studies. Among the 50 included references, 18 (36%) were published within the past five years (2021–2025), with the remainder retained as foundational methodological references for established techniques.

Outbreak detection, signal integration, and hypothesis generation

The investigative process is initiated when anomalous signals—clusters of clinically similar illness, unusual patterns in food microbiological monitoring, consumer complaint surges, or automated biosurveillance alerts—exceed pre-defined thresholds. In contrast to conventional frameworks that rely predominantly on clinical case detection, the proposed framework treats food and environmental monitoring programs as co-equal signal sources. Systematic routine testing of high-risk food categories, processing environment surveillance, and water quality monitoring can detect contamination events before clinical manifestations become apparent, enabling pre-symptomatic intervention—a capacity with significant public health leverage in outbreak scenarios involving pathogens with short incubation periods or high case fatality rates [22].

Signal integration draws on multiple concurrent data streams: microbiological surveillance data, antimicrobial resistance (AMR) profiling trends, foodomics-derived early warning indicators from food matrices, and epidemiological case clustering algorithms [23]. The formal process of hypothesis generation—identifying plausible food vehicles, contamination routes, and supply chain nodes—is structured around evidence triangulation from these sources, with explicit documentation of competing hypotheses and their evidential support. This structured approach directly improves the efficiency and targeting of downstream analytical activities, reducing both false-negative risks from premature hypothesis closure and the time-to-source-identification that determines outbreak control effectiveness.

Food and environmental sampling: design, execution, and special contexts

Sampling design represents the critical interface between hypothesis generation and laboratory analysis, and its quality directly determines the investigative value of all subsequent analytical activities. Sampling plans must be risk-stratified, accounting for pathogen-specific ecology, food matrix heterogeneity, contamination probability distributions along the supply chain, and the sensitivity requirements of downstream detection methods [24]. For low-prevalence pathogens such as L. monocytogenes, composite sampling strategies with statistical power calculations are essential to achieve adequate detection probabilities. The temporal dimension of sampling—capturing contamination at the moment of outbreak versus during remediation—requires coordinated, time-stamped collection protocols.

Environmental sampling within food production facilities serves as an indispensable complement to food sampling, enabling identification of harborage niches, cross-contamination routes, and persistent environmental reservoirs. Evidence from multiple outbreak investigations demonstrates that environmental persistence of L. monocytogenes in processing niches—drains, conveyor belt junctions, cold room door seals—constitutes a recurrent contamination source that can be identified and eliminated only through systematic environmental monitoring integrated with food isolate phylogenomics [25]. The framework recommends that environmental sampling be conducted in parallel with food sampling from the outset of investigations, and that environmental isolates be subjected to the same high-resolution molecular analysis as food isolates to enable phylogenomic convergence.

Informal food supply chains—encompassing traditional wet markets, street food vendors, mobile food sellers, and home-based food producers—present investigation challenges that are categorically distinct from those of formal, regulated food systems. These settings are characterized by: absence of formal traceability records or product lot coding; multiple simultaneous food sources with indeterminate provenance; inadequate temperature control and cold chain infrastructure; high vendor density and rapid product turnover; and limited regulatory oversight. These factors create an investigative environment in which standard trace-back procedures fail and in which contamination source attribution requires alternative approaches, including geo-tagged sampling surveys, vendor network mapping, phylogeographic analysis of pathogen isolates, and community-based epidemiological investigation. Dedicated sampling protocols for informal markets must account for rapid product turnover and the need for on-site, rapid screening methods to enable proportionate sampling of implicated products before they are sold or discarded [21]. The WHO FERG and FAO have recognized informal food systems as a major unaddressed gap in global food safety governance [1], and the present framework provides the first structured investigative approach for this context.

Conventional microbiological detection: foundation of the analytical hierarchy

Culture-based microbiological methods constitute the foundational tier of the framework’s analytical hierarchy, serving functions that no other method currently replicates: confirmation of pathogen viability, provision of isolates for phenotypic characterization and AMR susceptibility testing, and generation of legally defensible regulatory evidence [26]. ISO and AOAC standardized methods for selective enrichment, plating, and biochemical confirmation remain the regulatory reference for all major foodborne pathogens and are indispensable for enforcement actions including product recalls and facility closures. The framework treats culture as a non-negotiable first step, even when rapid molecular screening is applied in parallel, because molecular detection of pathogen DNA in a food matrix does not confirm the presence of viable, infectious organisms—a distinction critical for both risk characterization and regulatory action [27].

The principal limitation of culture-based methods—discriminatory resolution insufficient for source attribution in genomically diverse pathogen populations—is addressed within the framework by treating culture as the isolate generation step that enables molecular and genomic analysis. The turnaround time constraint (typically 2–5 days for enrichment and confirmation) is addressed through parallel deployment of rapid molecular screening assays during the enrichment phase, ensuring that actionable presumptive results are available within hours while culture confirmation proceeds. This parallelized approach, rather than sequential application, is a key architectural feature distinguishing the integrated framework from conventional sequential workflows.

Molecular detection and whole-genome sequencing: the core discriminatory engine

Polymerase chain reaction (PCR) and quantitative PCR (qPCR) assays provide the rapid screening capacity required for initial food vehicle exclusion and confirmation during the acute investigative phase [28]. Multiplex real-time PCR platforms targeting pathogen-specific virulence genes and serotype markers can deliver actionable results within 3–6 hours of sample receipt, enabling timely hypothesis confirmation or revision. For VBNC organisms and stressed cells—which evade culture-based detection but retain infection potential—direct PCR on food matrices provides critical detection capacity not available through conventional methods.

WGS has displaced pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) as the definitive method for outbreak strain discrimination and source attribution in high-resource settings [9–11, 29]. WGS-based SNP phylogenomics enables discrimination of strains separated by as few as 0–5 SNPs, providing resolution orders of magnitude greater than PFGE while simultaneously characterizing virulence genes, AMR determinants, plasmid content, and mobile genetic elements—data of direct relevance to both source attribution and risk characterization. National genomic surveillance networks, including GenomeTrakr (US), COMPARE (EU), and the Global Microbial Identifier initiative, have demonstrated that real-time WGS data sharing across regulatory and public health agencies can identify multinational outbreak clusters and implicated supply chains within days of initial case detection—a capability that fundamentally changes the epidemiology of outbreak investigation [9, 11]. The framework recommends WGS as the standard for source attribution whenever isolates are available, with bioinformatics pipelines standardized according to ECDC and FDA guidance to ensure cross-institutional comparability.

Foodomics: system-level intelligence for complex outbreak scenarios

Foodomics approaches extend outbreak investigation beyond single-pathogen, culture-dependent paradigms by enabling comprehensive molecular characterization of entire food ecosystems [12, 13]. Shotgun metagenomics applied directly to food matrices and environmental samples provides culture-independent detection and characterization of all microorganisms present, including pathogens in VBNC states, emerging or atypical pathogens for which culture methods may be unavailable, and complex polymicrobial contamination events [30]. Metagenomic source tracking algorithms, including SourceTracker2 and FEAST, enable probabilistic attribution of contamination to environmental reservoirs by comparing microbial community signatures between outbreak isolates and potential source environments—a capability with demonstrated utility in complex, multi-source outbreaks where WGS alone cannot resolve attribution [20].

Proteomics contributes to outbreak investigations through characterization of pathogen stress response proteins—including heat shock proteins, efflux pump overexpression, and biofilm-associated surface proteins—that indicate adaptation to specific food processing environments and predict persistence behavior relevant to contamination ecology and dose–response modeling [31]. Metabolomics provides complementary information on the biochemical milieu of contaminated food matrices, identifying metabolite signatures associated with pathogen growth, stress adaptation, and toxin production that can serve as early contamination indicators in food surveillance programs. The integration of multi-omics data streams through systems biology approaches—while currently at the frontier of research application rather than routine regulatory practice—represents the investigative direction most likely to resolve the most challenging outbreak scenarios: diffuse, multi-source contamination events in complex processed food matrices with low pathogen concentrations. The framework identifies foodomics as the advanced tier component for source attribution, to be deployed when conventional microbiological and WGS-based approaches fail to achieve attribution resolution.

Artificial intelligence and machine learning: augmenting the investigative capacity

The application of AI/ML to foodborne outbreak investigation represents one of the most rapidly evolving areas in food safety science, with demonstrated potential to fundamentally alter the speed and accuracy of source attribution. Supervised ML models trained on curated collections of WGS data with known source metadata have demonstrated accuracy rates of 80–95% in attributing Salmonella Typhimurium and Campylobacter isolates to their reservoir source (poultry, cattle, environment) from genomic signatures alone, without requiring epidemiological case linkage [32]. Random forest classifiers applied to pan-genome data have been used to predict the likely food category implicated in L. monocytogenes outbreaks based on genomic attributes of clinical and food isolates, dramatically accelerating the hypothesis generation stage of investigations [33].

Deep learning convolutional neural networks applied to whole metagenome sequencing data have shown promise for rapid pathogen detection and community profiling in complex food matrices, reducing analysis time from days to hours while maintaining sensitivity comparable to culture-based reference methods. Graph-based ML approaches for supply chain network analysis enable mathematical modeling of product flow through complex distribution networks, identification of high-risk nodes at which contamination events would generate the observed case distribution, and prioritization of trace-back investigations in scenarios where standard epidemiological methods cannot resolve the contamination source [16]. The integration of these tools into national surveillance systems—as exemplified by the FDA’s Genome-TrackR network and the EFSA’s One Health surveillance framework—demonstrates the regulatory and operational feasibility of AI-augmented outbreak investigation. The framework incorporates AI/ML as a core component of the advanced tier, with explicit acknowledgment of the data infrastructure, standardization, and algorithmic transparency requirements necessary for responsible regulatory deployment. Challenges in model interpretability (‘black box’ predictions) and the risk of systematic bias in training datasets derived predominantly from high-income country contexts must be explicitly addressed before AI/ML tools can be deployed equitably across food safety systems globally.

Geographic information systems and spatial epidemiology: mapping the contamination landscape

GIS and spatial epidemiology provide the geographic intelligence layer that connects microbiological and molecular findings to the physical and logistical architecture of food supply chains. In outbreak investigations, GIS-based spatial cluster analysis (using tools such as SaTScan, ArcGIS, or QGIS) enables identification of case hotspots, delineation of the geographic extent of contamination, and visualization of the relationship between case distribution and the geographic footprint of implicated food supply chains or production facilities [16]. The overlay of case maps with supply chain geographic data, product distribution records, and pathogen phylogeographic data—a technique termed integrated spatial genomic epidemiology—has been used to resolve the contamination source in complex multi-state and multinational outbreaks where epidemiological evidence alone was insufficient [21].

Spatial temporal modeling using kernel density estimation and space-time interaction tests enables real-time tracking of outbreak propagation fronts and identification of secondary contamination events arising from initial source dissemination. Environmental GIS layers—including watershed mapping, agricultural land use data, and climate variables—can inform contamination risk modeling for produce-associated outbreaks where environmental pathogen reservoirs play a critical role. The framework positions GIS as a core cross-cutting component, integrated at the hypothesis generation, source attribution, and risk communication stages. However, effective GIS-based investigation requires reliable geocoding of case residential and food purchase locations, digitized supply chain distribution records, and robust spatial data infrastructure—requirements that present significant implementation challenges in LMIC contexts where these data systems are often absent or incomplete. Capacity building in GIS for food safety purposes is identified as a priority investment for LMICs seeking to develop intermediate-tier investigation capabilities.

Source attribution and convergent evidence integration

Source attribution—the process of linking contaminated foods, environmental sources, and pathogen populations to specific supply chain nodes or production practices—represents the central analytical objective of outbreak investigation and the ultimate test of framework integration capacity. In the proposed framework, source attribution is achieved through convergent evidence integration: the systematic triangulation of epidemiological, microbiological, molecular, foodomics, GIS, and supply chain data to generate a weighted, probabilistic attribution that is continuously updated as new evidence becomes available [20, 34]. This convergent approach directly addresses the principal failure mode of single-method attribution: the inability of any individual data stream to resolve attribution in complex, multi-source scenarios with confounded evidence. WGS phylogenomics provides the strain-linkage scaffold; metagenomics source tracking provides ecosystem context; GIS supply chain analysis provides geographic plausibility weighting; and QMRA provides quantitative risk calibration of competing attribution hypotheses.

Harmonized bioinformatics workflows and standardized interpretation criteria are essential prerequisites for cross-institutional evidence convergence [20]. The framework recommends adoption of ECDC-harmonized WGS analysis pipelines (kSNP3, Snippy, or equivalent), standardized nomenclature systems (EnteroBase, MLST schemes), and transparent cluster definition thresholds (SNP cutoffs) to ensure that molecular evidence generated by different laboratories within and across jurisdictions can be meaningfully integrated. The absence of such harmonization—a persistent gap in current international practice—remains one of the principal barriers to timely multinational outbreak resolution. Table 1 provides a structured comparison of the three analytical tiers within the framework.

As illustrated in Table 1, the three analytical tiers are not alternatives but complementary layers whose integration generates investigative capacity exceeding the sum of their individual contributions. Conventional microbiology provides the legally defensible evidentiary base; molecular methods provide discriminatory resolution for attribution; and foodomics provides ecosystem intelligence for complex scenarios and novel contamination events. The transition from sequential to parallel and iterative deployment of these tiers is the defining architectural feature of the integrated framework. Table 2 summarizes the major foodborne pathogens, their principal food vehicles, epidemiological significance, and severe outcome profiles.

Table 2 highlights the heterogeneity of the foodborne pathogen landscape and underscores why a pathogen-agnostic, multi-method investigation framework is essential. The persistence of L. monocytogenes in processing environments and the high case fatality rate demand environmental surveillance integration not required for self-limiting Campylobacter outbreaks; the very high frequency of norovirus contamination demands molecular (RT-qPCR) screening methods unavailable through culture; and the substantial proportion of outbreaks with unidentified agents (~38–40%) demands metagenomics capacity for etiology-agnostic detection. No single-method framework can adequately serve this breadth of scenarios. The key references cited in Table 2 (WHO [1], EFSA [4], Scallan et al. [3]) have been supplemented with recent evidence where applicable. The epidemiological landscape described remains current as confirmed by the EFSA One Health Zoonoses Report 2022 [4] and the CDC Annual Surveillance Report 2022 [8], both published within the past five years. Recent systematic analyses further corroborate the frequency and severity data presented [17, 36].

Quantitative microbial risk assessment: dynamic integration

QMRA provides the quantitative bridge between microbiological evidence—pathogen concentration, prevalence, strain virulence characteristics—and public health outcome estimation (probability of illness, hospitalization, death) [37]. In the integrated framework, QMRA models are initialized at the hypothesis generation stage using published dose–response relationships and preliminary concentration estimates, generating an initial risk profile that prioritizes investigative resources toward the highest-risk hypotheses. As culture, molecular, and foodomics data accumulate, QMRA parameters are iteratively updated: culture-based enumeration refines exposure concentration estimates; WGS virulence profiling refines dose–response parameters for specific strains; and metabolomics data on pathogen stress adaptation informs survival probability modeling under food processing conditions [38–40]. This dynamic, Bayesian-framework-compatible approach transforms QMRA from a retrospective reporting tool into a real-time decision-support instrument.

The integration of stochastic Monte Carlo simulation into outbreak QMRA—using software platforms such as @RISK, ModelRisk, or the open-source MCRA platform—enables explicit quantification and communication of uncertainty at each investigative stage, providing decision-makers with probability distributions of outcomes rather than point estimates [37]. This is particularly valuable for communicating the risk implications of partial evidence to non-specialist decision-makers under time pressure.

Linking molecular evidence to hazard characterization

WGS-based virulence and AMR profiling provide hazard characterization data of direct relevance to QMRA that were unavailable to earlier investigation frameworks. The identification of virulence gene profiles (e.g., stx1/stx2 in STEC, hly/actA in L. monocytogenes, sopB/invA in Salmonella) from food isolates enables strain-specific dose–response estimation, enabling differentiation between high-virulence outbreak strains and background environmental contamination that would be conflated by culture-only analysis [39]. Similarly, AMR profiling identifies outbreak strains with resistance profiles that limit treatment options, elevating their clinical risk classification and potentially modifying the risk management response. Foodomics-derived metabolomic profiles of contaminated matrices—indicating biofilm formation capacity, heat resistance acquisition, or efflux pump upregulation—provide additional hazard characterization inputs that improve QMRA accuracy in complex food processing scenarios [40].

Risk management, regulatory decision-making, and proportionality

Risk management actions in outbreak contexts span a spectrum from consumer advisories and targeted voluntary recalls to mandatory product withdrawals, facility closures, and criminal enforcement proceedings. The proportionality of these actions to the evidence base and quantified risk magnitude is both an ethical imperative and a practical necessity: disproportionate responses incur economic harm, erode stakeholder trust, and may inhibit future voluntary cooperation with investigations [41]. The integrated framework supports proportionate decision-making by providing decision-makers with a continuously updated, evidence-graded risk characterization—distinguishing between ‘probable contamination source’ (phylogenomic linkage supported), ‘confirmed contamination source’ (culture and WGS convergence), and ‘exposure risk quantified’ (QMRA completed)—and by mapping these evidential grades to pre-defined management response protocols consistent with HACCP principles and Codex Alimentarius guidance [42]. The use of WGS evidence in enforcement proceedings has been upheld by regulatory agencies in the US, EU, and Australia, and its admissibility as legal evidence is increasingly established in international jurisprudence [9, 39].

Risk communication and uncertainty transparency

The communication of outbreak risk findings—including their uncertainty—to diverse audiences (public, industry, regulators, media) is a distinct professional competency that the framework explicitly addresses. Evidence from risk communication research consistently demonstrates that transparent acknowledgment of uncertainty, when framed constructively and accompanied by clear guidance on protective action, maintains rather than erodes public trust [41, 43]. Conversely, premature certainty claims that are subsequently revised generate severe credibility damage and long-term harm to the efficacy of risk communication. The framework recommends staged risk communication synchronized with investigative milestones: preliminary consumer guidance at hypothesis generation; targeted recall notifications at phylogenomic cluster confirmation; and comprehensive outbreak report at QMRA completion. Visualization tools—including GIS-generated contamination maps, risk characterization matrices, and probabilistic exposure figures—are integral to the communication strategy, enabling non-technical audiences to interpret geographically and temporally complex outbreak data [44].

Prevention integration and system-level learning

The ultimate measure of outbreak investigation quality is not speed of source identification but the degree to which investigation findings are translated into durable improvements in food system safety. The framework establishes a formal post-outbreak learning cycle in which root cause analysis—identifying the specific practices, equipment failures, supply chain vulnerabilities, or regulatory gaps that enabled the contamination event—generates targeted, evidence-based recommendations for prevention [35, 45–50]. These recommendations are mapped to specific intervention points in the HACCP framework, enabling food business operators to implement corrective actions within their existing food safety management systems rather than requiring the development of entirely new protocols. AI/ML models trained on aggregated outbreak investigation data can identify systemic risk patterns across food sectors and supply chains that are not apparent from individual investigation analyses, enabling prospective risk prioritization and resource allocation in food safety surveillance programs—a ‘learning system’ capacity that the framework is explicitly designed to support [32, 33].