Prevalence of Protozoan Pathogens Among Diarrheic Children Under 5 Years in Public Hospital of Ethiopia During the Global COVID 19 Pandemic.

Acute childhood diarrhea is one of the leading causes of childhood morbidity and mortality in sub-Saharan African countries. Entamoeba histolytica and Giardia lamblia are the common cause of childhood diarrhea in the region. However, there are only few studies on protozoa causing diarrhea in sub-Saharan African countries. This study was conducted to investigate the relative prevalence and explore risk factors of E. histolytica and G. lamblia among diarrheic children of under 5 years in a public hospital of Ethiopia. A retrospective study was conducted among diarrheic children at Hiwot Fana hospital, Ethiopia. Records of all diarrheic children less than 5 years who had sought medical treatment in the hospital from September 1, 2020 to December 31, 2022 were included. Data were collected from 1257 medical records of the children using a structured data-collection format. Data were entered into an Excel sheet and exported into SPSS version 22 for data processing and analysis. Descriptive statistical tests, Chi-square, and logistic region analysis were applied to determine predictors of protozoa infections. Of the 1257 cases, 962 (76.5%) had watery diarrhea and the remaining 239 (19.0%) had dysentery. The combined prevalence of E. histolytica and G. lamblia among diarrheic children was 11.8% (95% CI: 9.6-13.4). As the age of children increased, the frequency of these two protozoan infections was significantly increased compared to children with other causes. There were more diarrhea cases during the summer season including those associated with E. histolytica and G. lamblia. This study revealed that 1 in 10 causes of diarhhea among young children in the study area was likely caused by E. histolytica and G. lamblia. These findings call for community-based safe water and food safety interventions in order to reduce childhood diarrhea caused by protozoan infections in resource-poor settings.

Molecular Characterization of Hetero-Pathogenic and Diarrheagenic Escherichia coli Pathotypes in Diarrheic Children under Five Years and Exposure Environment in Ogun State, South-West Nigeria.

BACKGROUND: Diarrheagenic Escherichia coli (DEC) is one of the most common etiological agents of moderate-to-severe diarrhea in Low- and Middle-Income Countries (LMICs). Therefore, determining the source(s) of DEC in index cases and exposure environment is important for developing a prevention strategy. The current study aims to investigate the prevalence of DEC among children under 5 years and their exposure environment in Ogun State, Nigeria. METHODS: Samples from 228 diarrheic children and their exposure environment were collected and screened for E. coli. Bio-chemically compatible distinct colonies were molecularly characterized using a 7-virulence-gene multiplex PCR with virulence factors (VFs) indicative of four pathotypes of E. coli: enterotoxigenic (ETEC), verotoxigenic (VTEC), enteropathogenic (EPEC), and enteroinvasive (EIEC). Representative pathotypes were subjected to antimicrobial susceptibility and over-expressed efflux pump assays. RESULTS: One or more VFs typical of specific pathotypes were detected in 25.9% (59/228) diarrhea cases consisting of ETEC (21.5%) and EPEC (0.4%), while hetero-pathogenic pathotypes were found in 4.0% of cases. Of the food sources, 27.9% (101/362) were positive for DEC, of which ETEC accounted for 21.0%, VTEC 1.9%, EPEC 0.6%, EIEC 0.6%, and hetero-pathogenic pathotypes were 3.9%. Furthermore, ETEC was the only pathotype detected in the wastewater (4/183). Interestingly, the consumption of street-vended foods was the most significant (p = 0.04) risk factor for DEC infection in the study area. A total of 73.3% of selected DEC pathotypes showed resistance to antimicrobials, while 27.5% demonstrated over-expression of efflux pump activity. CONCLUSION: The high prevalence of ETEC across all sources and the occurrence of hetero-pathogenic DEC in diarrheic children and food sources emphasizes the importance of establishing a better strategy for the control and prevention of diarrhea among children in low- and medium-income households.

Analysis of reservoir sources of Campylobacter isolates to free-range broilers in Denmark.

Campylobacter is a common cause of food poisoning in many countries, with broilers being the main source. Organic and free-range broilers are more frequently Campylobacter-positive than conventionally raised broilers and may constitute a higher risk for human infections. Organic and free-range broilers may get exposed to Campylobacter from environmental reservoirs and livestock farms, but the relative importance of these sources is unknown. The aim of the study was to describe similarities and differences between the genetic diversity of the Campylobacter isolates collected from free-range/organic broilers with those isolated from conventional broilers and other animal hosts (cattle, pigs, and dogs) in Denmark to make inferences about the reservoir sources of Campylobacter to free-range broilers. The applied aggregated surveillance data consisted of sequenced Campylobacter isolates sampled in 2015 to 2017 and 2018 to 2021. The data included 1,102 isolates from free-range (n = 209), conventional broilers (n = 577), cattle (n = 261), pigs (n = 30), and dogs (n = 25). The isolates were cultivated from either fecal material (n = 434), food matrices (n = 569), or of nondisclosed origin (n = 99). Campylobacter jejuni (94.5%) dominated and subtyping analysis found 170 different sequence types (STs) grouped into 75 clonal complexes (CCs). The results suggest that CC-21 and CC-45 are the most frequent CCs found in broilers. The relationship between the CCs in the investigated sources showed that the different CCs were shared by most of the animals, but not pigs. The ST-profiles of free-range broilers were most similar to that of conventional broilers, dogs and cattle, in that order. The similarity was stronger between conventional broilers and cattle than between conventional and free-range broilers. The results suggest that cattle may be a plausible reservoir of C. jejuni for conventional and free-range broilers, and that conventional broilers are a possible source for free-range broilers or reflect a dominance of isolates adapted to the same host environment. Aggregated data provided valuable insight into the epidemiology of Campylobacter sources for free-range broilers, but time-limited sampling of isolates from different sources within a targeted area would hold a higher predictive value.

Occurrence of Diarrheagenic Pathogens and Their Coinfection Profiles in Diarrheic Under Five Children and Tracked Human Contacts in Urban and Rural Settings of Eastern Ethiopia.

Diarrheagenic Escherichia coli, Campylobacter, Nontyphoidal Salmonella, and Shigella are common cause of childhood diarrhea in countries like Ethiopia, but data on their sources and coinfection profiles is limited. A cross sectional study was conducted from November 2021 to January 2023 to determine the prevalence, coinfection, and monthly occurrence rates of major diarrheagenic bacteria in diarrheic under five children and asymptomatic contacts at urban and rural settings in Ethiopia. A total of 345 stool samples were collected from; 262 diarrheic children visiting Hiwot Fana Hospital, Kersa, and Adelle Health Centers; and 83 caretakers and siblings through case based contact tracing. Samples were analyzed using standard laboratory procedures and the overall prevalence of enteric pathogens was 26.96%, with the highest isolation rate during the winter and peaks of 73.91% in February. The occurrence of the pathogens in children and tracked contacts was 27.86 and 24.09%, respectively. In our study, 8.53% coinfection and 23.66% single pathogen infection was recorded in diarrheic children. The study also showed 4.51 and 3.88% of diarrhea in children from urban and rural had attributed to bacterial coinfection, respectively. The most prevalent pathogen in diarrheic children was Diarrheagenic E. coli (10.31%), and followed by Campylobacter. On the other hand, Diarrheagenic E. coli was the second dominant bacteria following Shigella in the traced contacts, with prevalence of 8.43% and 9.64%, respectively. Based on the study site, the prevalence of Diarrheagenic E. coli and Nontyphoidal Salmonella was higher in children from urban than those from rural. However, the occurrence of each pathogen had no significant differences (P > .05) between settings. The high pathogens occurrence rate in the current study indicates the need for strong control strategies and better child carrying and treatment of diarrheal diseases at both urban and rural settings. Further studies on possible sources and factors attributing to the occurrence of enteric pathogens in children are also recommended.

Comparison of Source Attribution Methodologies for Human Campylobacteriosis.

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.

Antimicrobial resistance monitoring in the Danish swine production by phenotypic methods and metagenomics from 1999 to 2018.

BackgroundIn Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways.AimThis study aimed to assess the relevance of indicator bacteria (Escherichia coli and Enterococcus faecalis) for AMR surveillance in pigs, and the utility of metagenomics.MethodsWe collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999-2004 and 2015-2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU.ResultsVia the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015-2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3-6 months.ConclusionWe validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.

Enteric Pathogens Detected in Children under Five Years Old Admitted with Diarrhea in Moshi, Kilimanjaro, Tanzania.

Despite the availability and wide coverage of rotavirus vaccinations in Tanzania, there is still a significant number of diarrhea cases being reported, with some patients requiring hospital admission. We investigated diarrhea-causing pathogens and determined the effect of co-infection on clinical symptoms. Total nucleic acid was extracted from archived stool samples (N = 146) collected from children (0-59 months) admitted with diarrhea in health facilities in Moshi, Kilimanjaro. Pathogen detection was performed using the quantitative polymerase chain reaction with custom TaqMan Array cards. The Poisson model was used to determine the effect of co-infection on clinical presentation during admission. Of all the participants, 56.85% were from rural Moshi with a median age of 11.74 months (IQR: 7.41-19.09). Vomiting (88.36%) and a fever (60.27%) were the most frequent clinical manifestations. At least one diarrhea-associated pathogen was detected in 80.14% (n = 117) of the study population. The most prevalent pathogens were rotavirus 38.36% (n = 56), adenovirus 40/41 19.86% (n = 29), Shigella/EIEC 12.33% (n = 18), norovirus GII 11.44% (n = 17) and Cryptosporidium 9.59% (n = 14). Co-infections were detected in 26.03% of the study population (n = 38). The presence of multiple pathogens in the stool samples of children with diarrhea indicates poor sanitation and may have significant implications for disease management and patient outcomes.

High diversity of Salmonella spp. from children with diarrhea, food, and environmental sources in Kilimanjaro - Tanzania: one health approach.

Salmonella is one of the most frequent causes of diarrhea globally. This study used a One Health approach to identify Salmonella species in children admitted with diarrhea and tested samples from the cases' household environment to investigate their genetic similarity using whole genome sequencing. Surveillance of hospitalized diarrhea cases among children under 5 years was conducted in rural and urban Moshi Districts in the Kilimanjaro Region of Tanzania from July 2020 through November 2022. Household visits were conducted for every child case whose parent/caregiver provided consent. Stool samples, water, domestic animal feces, meat, and milk were collected and tested for Salmonella. Isolates were sequenced on the Illumina NextSeq platform. Multilocus Sequence Typing and phylogenetic analyses were performed to map the genetic relatedness of the isolates. Salmonella was isolated from 72 (6.0%) of 1,191 samples. The prevalence of Salmonella in children with diarrhea, domestic animal feces, food, and water was 2.6% (n = 8/306), 4.6% (n = 8/174), 4.2% (n = 16/382), and 17.3% (n = 39/225), respectively. Four (1.3%) of the 306 enrolled children had a Salmonella positive sample taken from their household. The common sequence types (STs) were ST1208, ST309, ST166, and ST473. Salmonella Newport was shared by a case and a raw milk sample taken from the same household. The study revealed a high diversity of Salmonella spp., however, we detected a Salmonella clone of ST1208 isolated at least from all types of samples. These findings contribute to understanding the epidemiology of Salmonella in the region and provide insight into potential control of foodborne diseases through a One Health approach.

Sources and trends of human salmonellosis in Europe, 2015-2019: An analysis of outbreak data.

Salmonella remains a major cause of foodborne outbreaks in Europe despite the implementation of harmonized control programmes. Outbreak data are observed at the public health endpoint and provide a picture of the most important sources of human salmonellosis at the level of exposure. To prioritize interventions, it is important to keep abreast of the sources and trends of salmonellosis outbreaks. The objective of this study was to determine the main food sources and recent trends of Salmonella outbreaks in Europe. Salmonella outbreak data from 34 European countries in 2015-2019 were obtained from the European Food Safety Authority (EFSA). For the source attribution analysis, implicated foods were categorized according to EFSA's zoonosis catalogue classification scheme. An established probabilistic source attribution model was applied using the information on the implicated foods, overall and by region and serotype. To assess significant trends in outbreak occurrence, overall and by region and serotype, mixed-effects Poisson models were used. Overall, the most important food source of salmonellosis outbreaks was eggs (33 %, 95 % Uncertainty Interval [UI]: 31-36 %), followed by pork (7 %, 95 % UI: 6-8 %), and (general) meat products (6 %, 95 % UI: 5-8 %). While eggs were the most important food source in all regions, pork was the second most common food source in Northern and Western Europe, and (general) meat products in Eastern and Southern Europe. Outbreaks caused by S. Enteritidis (SE) and other known serotypes (other than SE and S. Typhimurium and its monophasic variant [STM]) were mostly attributed to eggs (37 %, 95 % UI: 34-41 % and 17 %, 95 % UI: 11-25 %, respectively), whereas outbreaks caused by STM were mainly attributed to pork (34 %, 95 % UI: 27-42 %). Overall, there was a significant increase in the number of outbreaks reported between 2015 and 2019, by 5 % on average per year (Incidence Rate Ratio [IRR]: 1.05, 95 % Confidence Interval [CI]: 1.01-1.09). This was driven by a significantly increased number of outbreaks in Eastern Europe, particularly those caused by SE (IRR: 1.15, 95 % CI: 1.09-1.22), whereas in Northern and Southern Europe, outbreaks caused by SE decreased significantly from 2015 to 2019 (IRR: 0.72, 95 % CI: 0.61-0.85; IRR: 0.70, 95 % CI: 0.62-0.79, respectively). Regional, temporal and serotype-associated differences in the relative contributions of the different sources were also observed.

System Mapping of Antimicrobial Resistance to Combat a Rising Global Health Crisis.

Antimicrobial resistance (AMR) decreases the effectiveness of antimicrobials to treat bacterial infections in humans and animals. The increased occurrence of AMR in bacterial population in humans, animals, and the environment requires the measures to combat a rising global health crisis. The aim of this research was to present current knowledge on AMR in a system map and to identify potential explanations of former identified variables significantly associated with AMR. This study applies a systems thinking approach and uses feedback loops to visualize the interconnections between human, animal, and environmental components in a circular AMR system map model. First, a literature review focusing on AMR and socioeconomic factors, wicked problem, and system change was carried out, which was then processed in a system map to conceptualize the present core challenges of AMR via feedback loops. Second, to investigate possible underlying values of the society and those that influence humans' behavior in the present AMR system, an iceberg model was established. Third, leverage points were assessed to estimate which kinds of interventions would have the greatest effect to mitigate AMR in the system. The present AMR system map implies the potential to identify and visualize important risk factors that are direct or indirect drivers of AMR. Our results show that the tool of system mapping, which interconnects animals, humans, and environment in one model, can approach AMR holistically and be used to assess potential powerful entry points for system wide interventions. This study shows that system maps are beneficial as a model to predict the relative effect of different interventions and adapt to rapidly changing environments in a complex world. Systems thinking is considered as a complementing approach to the statistical thinking, and further research is needed to evaluate the use of such tools for the development and monitoring of interventions.

Practicalities of implementing burden of disease research in Africa: lessons from a population survey component of our multi-partner FOCAL research project.

BACKGROUND: Collaborative research is being increasingly implemented in Africa to study health-related issues, for example, the lack of evidence on disease burden, in particular for the presumptive high load of foodborne diseases. The FOCAL (Foodborne disease epidemiology, surveillance, and control in African LMIC) Project is a multi-partner study that includes a population survey to estimate the foodborne disease burden in four African low- and middle-income countries (LMICs). Our multi-partner study team had members from seven countries, all of whom contributed to the project from the grant application stage, and who play(ed) specific roles in designing and implementing the population survey. MAIN TEXT: In this paper, we applied Larkan et al.'s framework for successful research partnerships in global health to self-evaluate our project's collaboration, management, and implementation process. Our partnership formation considered the interplay and balance between operations and relations. Using Larkan et al.'s seven core concepts (i.e., focus, values, equity, benefit, communication, leadership, and resolution), we reviewed the process stated above in an African context. CONCLUSION: Through our current partnership and research implementing a population survey to study disease burden in four African LMICs, we observed that successful partnerships need to consider these core concepts explicitly, apply the essential leadership attributes, perform assessment of external contexts before designing the research, and expect differences in work culture. While some of these experiences are common to research projects in general, the other best practices and challenges we discussed can help inform future foodborne disease burden work in Africa.

Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis.

Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input.

Prevalence and epidemiological distribution of selected foodborne pathogens in human and different environmental samples in Ethiopia: a systematic review and meta-analysis.

Bacterial Foodborne Pathogens (FBP) are the commonest cause of foodborne illness or foodborne diseases (FBD) worldwide. They contaminate food at any stages in the entire food chain, from farm to dining-table. Among these, the Diarrheagenic Escherichia coli (DEC), Non typhoidal Salmonella (NTS), Shigella spp. and Campylobacter spp. are responsible for a large proportion of illnesses, deaths; and, particularly, as causes of acute diarrheal diseases. Though existing studies indicate the problem may be severe in developing countries like Ethiopia, the evidence is commonly based on fragmented data from individual studies. A review of published and unpublished manuscripts was conducted to obtain information on major FBP and identify the gaps in tracking their source attributions at the human, animal and environmental interface. A total of 1753 articles were initially retrieved after restricting the study period to between January 2000 and July 2020. After the second screening, only 51 articles on the humans and 43 on the environmental sample based studies were included in this review. In the absence of subgroups, overall as well as human stool and environmental sample based pooled prevalence estimate of FBP were analyzed. Since, substantial heterogeneity is expected, we also performed a subgroup analyses for principal study variables to estimate pooled prevalence of FBP at different epidemiological settings in both sample sources. The overall random pooled prevalence estimate of FBP (Salmonella, pathogenic Escherichia coli (E. coli), Shigella and Campylobacter spp.) was 8%; 95% CI: 6.5-8.7, with statistically higher (P <  0.01) estimates in environmental samples (11%) than in human stool (6%). The subgroup analysis depicted that Salmonella and pathogenic E. coli contributed to 5.7% (95% CI: 4.7-6.8) and 11.6% (95% CI: 8.8-15.1) respectively, of the overall pooled prevalence estimates of FBD in Ethiopia. The result of meta-regression showed, administrative regional state, geographic area of the study, source of sample and categorized sample size all significantly contributed to the heterogeneity of Salmonella and pathogenic E. coli estimates. Besides, the multivariate meta- regression indicated the actual study year between 2011 and 2015 was significantly associated with the environmental sample-based prevalence estimates of these FBP. This systematic review and meta-analysis depicted FBP are important in Ethiopia though majority of the studies were conducted separately either in human, animal or environmental samples employing routine culture based diagnostic method. Thus, further FBD study at the human, animal and environmental interface employing advanced diagnostic methods is needed to investigate source attributions of FBD in one health approach.

Burden of foodborne diseases: think global, act local.

National burden of foodborne disease (FBD) studies are essential to establish food safety as a public health priority, rank diseases, and inform interventions. In recent years, various countries have taken steps to implement them. Despite progress, the current burden of disease landscape remains scattered, and researchers struggle to translate findings to input for policy. We describe the current knowledge base on burden of FBDs, highlight examples of well-established studies, and how results have been used for decision-making. We discuss challenges in estimating burden of FBD in low-resource settings, and the experience and opportunities deriving from a large-scale research project in these settings. Lastly, we highlight the role of international organizations and initiatives in supporting countries to develop capacity and conduct studies.

Attribution of Illnesses Transmitted by Food and Water to Comprehensive Transmission Pathways Using Structured Expert Judgment, United States.

Illnesses transmitted by food and water cause a major disease burden in the United States despite advancements in food safety, water treatment, and sanitation. We report estimates from a structured expert judgment study using 48 experts who applied Cooke's classical model of the proportion of disease attributable to 5 major transmission pathways (foodborne, waterborne, person-to-person, animal contact, and environmental) and 6 subpathways (food handler-related, under foodborne; recreational, drinking, and nonrecreational/nondrinking, under waterborne; and presumed person-to-person-associated and presumed animal contact-associated, under environmental). Estimates for 33 pathogens were elicited, including bacteria such as Salmonella enterica, Campylobacter spp., Legionella spp., and Pseudomonas spp.; protozoa such as Acanthamoeba spp., Cyclospora cayetanensis, and Naegleria fowleri; and viruses such as norovirus, rotavirus, and hepatitis A virus. The results highlight the importance of multiple pathways in the transmission of the included pathogens and can be used to guide prioritization of public health interventions.

Non-typhoidal human salmonellosis in Rio Grande do Sul, Brazil: A combined source attribution study of microbial subtyping and outbreak data.

Salmonella spp. remains the most significant foodborne pathogen in south Brazil, but its epidemiology tends to change over time. Using official and surrogate data, a microbial subtyping model attributed different Salmonella serovars to laying hens, pigs, broilers, and turkeys from 2005 to 2015 in Rio Grande do Sul (RS). Additional to the subtyping model, three sub-analyses of outbreak data attributed Salmonella spp. in humans to animal and non-animal food. Laying hens/eggs was the most important source of human salmonellosis in RS, with almost 40% (159 cases; 95% credibility interval, 43-247) attribution proportion, followed by pigs reared in Santa Catarina, a neighbor state (34.5%). The Salmonella serovars Enteritidis and Typhimurium were the most common serovars involved. Source-related parameters had wide credibility intervals but showed a higher risk of illness from contaminated eggs than from the other three animal-food sources. Analysis of the outbreak data corroborated the findings and indicated signs of decreasing importance for eggs and increasing importance for pork consumption.

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes.

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77-0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92-0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706-0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.

AB_SA: Accessory genes-Based Source Attribution - tracing the source of Salmonella enterica Typhimurium environmental strains.

The partitioning of pathogenic strains isolated in environmental or human cases to their sources is challenging. The pathogens usually colonize multiple animal hosts, including livestock, which contaminate the food-production chain and the environment (e.g. soil and water), posing an additional public-health burden and major challenges in the identification of the source. Genomic data opens up new opportunities for the development of statistical models aiming to indicate the likely source of pathogen contamination. Here, we propose a computationally fast and efficient multinomial logistic regression source-attribution classifier to predict the animal source of bacterial isolates based on 'source-enriched' loci extracted from the accessory-genome profiles of a pangenomic dataset. Depending on the accuracy of the model's self-attribution step, the modeller selects the number of candidate accessory genes that best fit the model for calculating the likelihood of (source) category membership. The Accessory genes-Based Source Attribution (AB_SA) method was applied to a dataset of strains of Salmonella enterica Typhimurium and its monophasic variant (S. enterica 1,4,[5],12:i:-). The model was trained on 69 strains with known animal-source categories (i.e. poultry, ruminant and pig). The AB_SA method helped to identify 8 genes as predictors among the 2802 accessory genes. The self-attribution accuracy was 80 %. The AB_SA model was then able to classify 25 of the 29 S. enterica Typhimurium and S. enterica 1,4,[5],12:i:- isolates collected from the environment (considered to be of unknown source) into a specific category (i.e. animal source), with more than 85 % of probability. The AB_SA method herein described provides a user-friendly and valuable tool for performing source-attribution studies in only a few steps. AB_SA is written in R and freely available at https://github.com/lguillier/AB_SA.