Increasing incubation periods during a prolonged monophasic Salmonella Typhimurium outbreak with environmental contamination of a commercial kitchen at Oslo Airport, Norway, 2017

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

Validating the Utility of Multilocus Variable Number Tandem-repeat Analysis (MLVA) as a Subtyping Strategy to Monitor Listeria monocytogenes In-built Food Processing Environments

Listeria monocytogenes is a serious human pathogen and an enduring challenge to control for the ready-to-eat food processing industry. Cost-effective tools that can be deployed by commercial or in-house laboratories to rapidly investigate and resolve contamination events in the built food processing environment are of value to the food industry. Multilocus variable number tandem-repeat analysis (MLVA) is a molecular subtyping method, which along with other same-generation methods such as pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) is being superseded in disease tracking and outbreak investigations by whole-genome sequencing (WGS). In this paper, it is demonstrated that MLVA can continue to play a valuable role as a valid, fast, simple, and cost-effective method to identify and track Listeria monocytogenes subtypes in factory environments, with the method being highly congruent with MLST. Although MLVA does not have the discriminatory power of WGS to identify truly persistent clones, with careful interpretation of results alongside isolate metadata, it remains a powerful tool in situations and locations where WGS may not be readily available to food business operators.

Investigation of a Salmonella Montevideo Outbreak Related to the Environmental Contamination of a Restaurant Kitchen Drainage System, Québec, Canada, 2020-2021

In May 2020, the Direction de santé publique du CIUSSS de la Capitale-Nationale (DSPu) received a report from the Laboratoire de santé publique du Québec of a cluster of three cases of Salmonella enterica enterica, serogroup C1, serotype Montevideo. The epidemiological investigation identified a total of 67 cases between January 1, 2020, and August 13, 2021, 66% of which were directly linked to a restaurant in the area. The Salmonella strains from most of these cases were found to be identical by whole-genome sequencing (cluster code 2005MontWGS-1QC). The initial inspection of the restaurant by the competent authorities (Ministère de l'agriculture, des pêcheries et de l'alimentation du Québec) - including the evaluation of hygiene and food safety, the search for cases of illness among workers and food sampling - was unable to establish the source of the outbreak. Environmental samples showed that the restaurant's kitchen drains were contaminated with the same strain of Salmonella Montevideo as the cases in the outbreak. Several cleaning and disinfection methods were used repeatedly. When environmental sampling at the restaurant sites was repeatedly and consecutively negative, cases in the community stopped. The prior occurrence of a fire in the kitchen may have played a role in the contamination of the restaurant drains. In conclusion, public health professionals should consider drainage systems (plumbing) and possible aerosolization of bacteria as a potential source of a restaurant-related salmonellosis outbreak.

Dynamics of Antimicrobial Resistance and Genomic Epidemiology of Multidrug-Resistant Salmonella enterica Serovar Indiana ST17 from 2006 to 2017 in China

The genetic features of foodborne Salmonella have changed in recent years as multidrug-resistant (MDR) strains have become prevalent among various serovars. The recent expansion of MDR Salmonella enterica serovar Indiana sequence type 17 (ST17) poses an increasing threat to global public health, as 24.3% (61/251) of S. Indiana isolates in this study exhibited resistance to three clinically important antimicrobial agents: fluoroquinolones (ciprofloxacin), extended-spectrum β-lactams (cephalosporin), and macrolides (azithromycin). Both the evolutionary histories and antimicrobial resistance (AMR) profiles of this serovar remain to be described. Bioinformatic analysis revealed multiple lineages have coexisted and spread throughout China. Specifically, emergence of a predominant lineage appears to be associated with accumulated various substitutions in the chromosomal quinolone resistance-determining regions (GyrA S83F D87N and ParC T57S S80R) (141 [56.2%]), as well as acquisition of an extended-spectrum β-lactamase (ESBL)-producing IncHI2 plasmid that has subsequently undergone extensive rearrangement and an IncX1 plasmid that contains mph(A), conferring resistance to azithromycin. Several other evolutionary events influencing the trajectory of this drug-resistant serovar were also identified, including sporadic acquisitions of bla_CTX-M-carrying plasmids, along with chromosomal integration of bla_CTX-M within subclusters. Most human isolates reside in clusters containing isolates from animals, mainly from chickens, indicating the close relationship of human isolates with those from food animals. These data demonstrate that MDR S. Indiana ST17 is already widespread and capable of acquiring resistance traits against the clinical important antimicrobial agents, suggesting it should be considered a high-risk global MDR pathogen. The complexity of its evolutionary history has implications for AMR surveillance, epidemiological analysis, and control of emerging clinical lineages.

IMPORTANCE The emergence and worldwide spread of AMR Salmonella constitute great public health concerns. S. enterica serovar Indiana is a typical MDR serovar characterized by sporadic reports. However, comprehensive population genomics studies have not been performed on this serovar. This study provides a detailed and comprehensive insight into the rapid evolution of AMR in this important Salmonella serovar in the past 15 years in eight provinces of China. We documented diverse contributory genetic processes, including stable chromosomal integrations of resistance genes, the persistence and evolution of mobile resistance elements within sublineages, and sporadic acquisition of different resistance determinants that occur at all genetic levels (genes, genetic contexts, plasmids, and host strains). There are different mechanisms of antimicrobial resistance in S. enterica serovar Indiana from those of other serovars. This study sheds light on the formation of MDR S. enterica serovar Indiana with chickens as its potential reservoirs and paves the way to curb its further expansion among food animals.