An Overview of Foodborne Sample-Initiated Retrospective Outbreak Investigations and Interagency Collaboration in the United States

Foodborne Disease Outbreaks Caused by Biotoxins in Yantai City: A 10-Year Spatiotemporal Monitoring Study

Unsafe food causes 600 million cases of foodborne diseases and 420,000 deaths every year. Meanwhile, biological toxins such as poisonous mushrooms, saponins, and aflatoxin can cause significant damage to humans. Therefore, it is particularly important to study foodborne disease outbreaks caused by biotoxins (FDOB). We collected FDOB in Yantai City from 2013 to 2022 and further established a corresponding database. Statistical analysis was carried out according to time, place, pathogen, and contamination of pathogenic factors. There were 128 FDOB, resulting in 417 patients and 6 deaths. The third quarter was a high season for foodborne disease outbreaks, the number of events, patients and deaths accounted for 65.63% (84/128), 55.88% (233/417), and 100% (6/6) of the total number, respectively. The highest number of outbreaks per 10,000 persons was Qixia (0.41), followed by Zhifu (0.36) and Laiyang (0.33). The top three causes of outbreaks were poisonous mushroom toxin, saponins and hemagglutinin, and Lagenaria siceraria (Molina) Standl. Sixty-five (50.78%) outbreaks were attributed to poisonous mushroom toxin, 18 (14.06%) outbreaks to saponin and hemagglutinin, and 12 (9.38%) outbreaks to L. siceraria (Molina) Standl. The largest number of outbreaks, patients and deaths all occurred in families, accounting for 82.81% (106/128) outbreaks, 66.19% (276/417) patients, and 100% (6/6) deaths, respectively. Followed by catering service establishments, accounting for 14.84% (19/128), 30.22% (126/417), and 0% (0/6), respectively. The main poisoning link of outbreaks was ingestion and misuse, accounting for 72.66% (93/128), followed by improper processing, accounting for 20.31% (26/128). It is necessary to carry out targeted family publicity and education, strengthen the integration of medical and prevention, explore innovative monitoring and early warning mechanisms for foodborne diseases, and reduce the occurrence of underreporting of foodborne disease outbreaks.

Comprehensive Review on the Biocontrol of Listeria monocytogenes in Food Products

Listeria monocytogenes is a foodborne pathogen that causes listeriosis, a group of human illnesses that appear more frequently in countries with better-developed food supply systems. This review discusses the efficacy of actual biocontrol methods combined with the main types of food involved in illnesses. Comments on bacteriophages, lactic acid bacteria, bacteriocins, essential oils, and endolysins and derivatives, as main biological antilisterial agents, are made bearing in mind that, using them, food processors can intervene to protect consumers. Both commercially available antilisterial products and solutions presented in scientific papers for mitigating the risk of contamination are emphasized. Potential combinations between different types of antilisterial agents are highlighted for their synergic effects (bacteriocins and essential oils, phages and bacteriocins, lactic acid bacteria with natural or synthetic preservatives, etc.). The possibility to use various antilisterial biological agents in active packaging is also presented to reveal the diversity of means that food processors may adopt to assure the safety of their products. Integrating biocontrol solutions into food processing practices can proactively prevent outbreaks and reduce the occurrences of L. monocytogenes-related illnesses.

Predicting Food Sources of Listeria monocytogenes Based on Genomic Profiling Using Random Forest Model

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.