Foodborne Disease Outbreak Investigations Research Articles

Outbreak Linked to Morel Mushroom Exposure - Montana, 2023.

During March-April 2023, a total of 51 persons reported mild to severe gastrointestinal illness after eating at restaurant A in Bozeman, Montana. The outbreak resulted in multiple severe outcomes, including three hospitalizations and two deaths. After an inspection and temporary restaurant closure, the Montana Department of Public Health and Human Services and Montana's Gallatin City-County Health Department collaborated with CDC to conduct a matched case-control study among restaurant patrons to help identify the source of the outbreak. Consumption of morel mushrooms, which are generally considered edible, was strongly associated with gastrointestinal illness. A dose-response relationship was identified, and consumption of raw morel mushrooms was more strongly associated with illness than was consumption of those that were at least partially cooked. In response to the outbreak, educational public messaging regarding morel mushroom preparation and safety was shared through multiple media sources. The investigation highlights the importance of prompt cross-agency communication and collaboration, the utility of epidemiologic studies in foodborne disease outbreak investigations, and the need for additional research about the impact of morel mushroom consumption on human health. Although the toxins in morel mushrooms that might cause illness are not fully understood, proper preparation procedures, including thorough cooking, might help to limit adverse health effects.

A Bayesian Method for Exposure Prevalence Comparison During Foodborne Disease Outbreak Investigations.

CDC and health departments investigate foodborne disease outbreaks to identify a source. To generate and test hypotheses about vehicles, investigators typically compare exposure prevalence among case-patients with the general population using a one-sample binomial test. We propose a Bayesian alternative that also accounts for uncertainty in the estimate of exposure prevalence in the reference population. We compared exposure prevalence in a 2020 outbreak of Escherichia coli O157:H7 illnesses linked to leafy greens with 2018-2019 FoodNet Population Survey estimates. We ran prospective simulations using our Bayesian approach at three time points during the investigation. The posterior probability that leafy green consumption prevalence was higher than the general population prevalence increased as additional case-patients were interviewed. Probabilities were >0.70 for multiple leafy green items 2 weeks before the exact binomial p-value was statistically significant. A Bayesian approach to assessing exposure prevalence among cases could be superior to the one-sample binomial test typically used during foodborne outbreak investigations.

First molecular characterization of Escherichia coli O157:H7 isolates from clinical samples in Paraguay using whole-genome sequencing

Escherichia coli O157:H7 is a foodborne pathogen implicated in numerous outbreaks worldwide that has the ability to cause extra-intestinal complications in humans. The Enteropathogens Division of the Central Public Health Laboratory (CPHL) in Paraguay is working to improve the genomic characterization of Shiga toxin-producing E. coli (STEC) to enhance laboratory-based surveillance and investigation of foodborne disease outbreaks. Whole genome sequencing (WGS) is proposed worldwide to be used in the routine laboratory as a high-resolution tool that allows to have all the results in a single workflow. This study aimed to carry out for the first time, the genomic characterization by WGS of nine STEC O157:H7 strains isolated from human samples in Paraguay. We were able to identify virulence and resistance mechanisms, MLST subtype, and even establish the phylogenetic relationships between isolates. Furthermore, we detected the presence of strains belonging to hypervirulent clade 8 in most of the isolates studied.

Improving Foodborne Disease Surveillance and Outbreak Detection and Response Using Peer Networks-The Integrated Food Safety Centers of Excellence.

Foodborne disease surveillance and outbreak investigations are foundational to the prevention and control of foodborne disease in the United States, where contaminated foods cause an estimated 48 million illnesses, 128 000 hospitalizations, and 3000 deaths each year. Surveillance activities and rapid detection and investigation of foodborne disease outbreaks require a trained and coordinated workforce across epidemiology, environmental health, and laboratory programs. Under the 2011 Food Safety Modernization Act, the Centers for Disease Control and Prevention (CDC) was called on to establish Integrated Food Safety (IFS) Centers of Excellence (CoEs) at state health departments, which would collaborate with academic partners, to identify, implement, and evaluate model practices in foodborne disease surveillance and outbreak response and to serve as a resource for public health professionals. CDC designated 5 IFS CoEs in August 2012 in Colorado, Florida, Minnesota, Oregon, and Tennessee; a sixth IFS CoE in New York was added in August 2014. For the August 2019-July 2024 funding period, 5 IFS CoEs were designated in Colorado, Minnesota, New York, Tennessee, and Washington. Each IFS CoE is based at the state health department that partners with at least one academic institution. IFS CoEs have built capacity across public health agencies by increasing the number of workforce development opportunities (developing >70 trainings, tools, and resources), supporting outbreak response activities (responding to >50 requests for outbreak technical assistance annually), mentoring students, and responding to emerging issues, such as changing laboratory methods and the COVID-19 pandemic.

Evaluation of the use of CRISPR loci for discrimination of Salmonella enterica subsp. enterica serovar Enteritidis strains recovered in Canada and comparison with other subtyping methods.

Salmonella enterica subsp. enterica serovar Enteritidis remains one of the most important foodborne pathogens worldwide. To minimise its public health impact when outbreaks of the disease occur, timely investigation to identify and recall the contaminated food source is necessary. Central to this approach is the need for rapid and accurate identification of the bacterial subtype epidemiologically linked to the outbreak. While traditional methods of S. Enteritidis subtyping, such as pulsed field gel electrophoresis (PFGE) and phage typing (PT), have played an important role, the clonal nature of this organism has spurred efforts to improve subtyping resolution and timeliness through molecular based approaches. This study uses a cohort of 92 samples, recovered from a variety of sources, to compare these two traditional methods for S. Enteritidis subtyping with recently developed molecular techniques. These latter methods include the characterisation of two clustered regularly interspaced short palindromic repeats (CRISPR) loci, either in isolation or together with sequence analysis of virulence genes such as fimH. For comparison, another molecular technique developed in this laboratory involved the scoring of 60 informative single nucleotide polymorphisms (SNPs) distributed throughout the genome. Based on both the number of subtypes identified and Simpson's index of diversity, the CRISPR method was the least discriminatory and not significantly improved with the inclusion of fimH gene sequencing. While PT analysis identified the most subtypes, the SNP-PCR process generated the greatest index of diversity value. Combining methods consistently improved the number of subtypes identified, with the SNP/CRISPR typing scheme generating a level of diversity comparable with that of PT/PFGE. While these molecular methods, when combined, may have significant utility in real-world situations, this study suggests that CRISPR analysis alone lacks the discriminatory capability required to support investigations of foodborne disease outbreaks.

A systematic review on mobile health applications for foodborne disease outbreak management

BackgroundFoodborne disease outbreaks are common and notifiable in South Africa; however, they are rarely reported and poorly investigated. Surveillance data from the notification system is suboptimal and limited, and does not provide adequate information to guide public health action and inform policy. We performed a systematic review of published literature to identify mobile application-based outbreak response systems for managing foodborne disease outbreaks and to determine the elements that the system requires to generate foodborne disease data needed for public action.MethodsStudies were identified through literature searches using online databases on PubMed/Medline, CINAHL, Academic Search Complete, Greenfile, Library, Information Science & Technology. Search was limited to studies published in English during the period January 1990 to November 2020. Search strategy included various terms in varying combinations with Boolean phrases “OR” and “AND”. Data were collected following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement. A standardised data collection tool was used to extract and summarise information from identified studies. We assessed qualities of mobile applications by looking at the operating system, system type, basic features and functionalities they offer for foodborne disease outbreak management.ResultsFive hundred and twenty-eight (528) publications were identified, of which 48 were duplicates. Of the remaining 480 studies, 2.9% (14/480) were assessed for eligibility. Only one of the 14 studies met the inclusion criteria and reported on one mobile health application named MyMAFI (My Mobile Apps for Field Investigation). There was lack of detailed information on the application characteristics. However, based on minimal information available, MyMAFI demonstrated the ability to generate line lists, reports and offered functionalities for outbreak verification and epidemiological investigation. Availability of other key components such as environmental and laboratory investigations were unknown.ConclusionsThere is limited use of mobile applications on management of foodborne disease outbreaks. Efforts should be made to set up systems and develop applications that can improve data collection and quality of foodborne disease outbreak investigations.

Foodborne Illness Outbreak Investigation for One Health Postsecondary Education.

ABSTRACTOne Health concepts were incorporated in a foodborne disease outbreak investigation with game features of data presented as visual and manipulative clues. Postsecondary pre-veterinary medicine and animal biosciences students and food science students (n = 319) enrolled in an introductory animal and food sciences course over a 3-year period received a brief introduction to foodborne illness, an outbreak scenario, and investigative tasks to complete individually or in groups. Tasks addressed epidemiology, laboratory, environment, traceback, recall, and prevention concepts. Gamification of the exercise involved generation of a numerical code to unlock a combination lock as an indication of successful organization, compilation, and interpretation of data. Students presented investigation findings and responses to critical thought questions on their roles. Student surveys on engagement and self-perceived change in conceptual understanding indicated that nearly all expressed increased understanding of outbreak investigations, safe food production, and environmental water as a transmission vehicle. Volunteered learned concepts indicated enhanced appreciation for the complexity of food safety and interdisciplinary connections. Students enjoyed the exercise (92%) and cited the clues and group interaction among the most enjoyable features. Objective assessment of student conceptual learning with the subset of students who conducted the investigation individually (n = 58) demonstrated significant increase in correct test responses (49% pretest; 76% posttest) after completion of the investigation for all questions combined and across all learning objectives. These data demonstrate the value of a foodborne disease investigation with escape room gamification features for engaging students in One Health concepts and exercising problem-solving, critical thinking, and skills for independent and collaborative work.

Foods Implicated in U.S. Outbreaks Differ from the Types Most Commonly Consumed

Foodborne disease outbreak investigations identify foods responsible for illnesses. However, it is not known the degree to which foods implicated in outbreaks reflect the distribution of food consumption in the U.S. population or the risk associated with their consumption. We compared the distribution of 24 categories of foods implicated in outbreaks with the distribution of foods consumed by the U.S. population. Beef, chicken, eggs, fish, herbs, mollusks, pork, sprouts, seeded vegetables, and turkey were implicated in outbreaks significantly more often than expected based on the frequency of their consumption by the general population, suggesting a higher risk of contamination or mishandling from foods in these categories than from foods in other categories. In contrast, pasteurized dairy, fruits, grains and beans, oils and sugars, and root and underground vegetables were less frequently implicated in outbreaks than their frequency of consumption by the general population, suggesting a lower health risk associated with these food categories.

Knowledge, attitude and practices of environmental health practitioners conducting food-borne disease outbreak investigation at a local municipality in Gauteng province, South Africa

BackgroundFood-borne disease (FBD) outbreaks are a common occurrence that is either not investigated or poorly investigated. According to anecdote evidence, this is because of non-uniformity to environmental health practices in South Africa.AimThis study aimed to determine and describe the knowledge, attitude and practices (KAP) of environmental health practitioners (EHPs) when conducting outbreak investigations of FBD at a local municipality.SettingThis study was conducted in three sub-districts of Ekurhuleni Metropolitan Municipality (EMM), one of five municipalities in Gauteng province, South Africa.MethodsA descriptive cross-sectional study was conducted to collect data using a semi-structured questionnaire. Data collected were analysed using IBM Statistical Package for the Social Sciences. Continuous variables were compared using analysis of variances, and correlation was used to determine any association.ResultsKnowledge responses were scored out of 9. Open-ended questions were themed into five items (support, guidelines, resources, training and specialisation). Sixty-one (76.3%) participants were randomly selected to participate in the study. There were more female participants (55.7%) than male participants, and the mean age was 30.9 years. The participants’ knowledge scores ranged from 1 to 9. There were 17 (27.9%) participants who have conducted FBD outbreak investigation. Twenty-six (42.6%) believed that they were properly trained to conduct FBD outbreak investigations. Age was associated with position (p = 0.000) and qualification (p = 0.033).ConclusionThe results indicated that there are gaps and challenges in the knowledge, while the practices were not consistent amongst environmental health practitioners. However, the attitude of EHPs was positive with regard to their role in FBD outbreak investigations.

Population-based food consumption survey as an additional tool for foodborne outbreak investigations, Germany, 2017.

We conducted a food consumption survey in the general adult population of 18 years and older in Germany to obtain data on the frequency of consumption of food items that caused foodborne disease outbreaks in the past. A total of 1010 telephone interviews were completed that queried the consumption of 95 food items in the 7-day period before the interview. Survey results were weighted to be representative. Six exemplary 'high risk' food items were consumed by 6% to 16% of the general population. These were raw ground pork: 6.5%; 'Teewurst' (=spreadable sausage-containing raw pork): 15.7%; unpasteurised milk consumed without prior heating: 9.0%; food items prepared with raw eggs: 9.8%; unheated sprouts or seedlings: 8.8% and frozen berries consumed without prior heating: 6.2%. Data from our food consumption survey were comparable to data obtained from control persons in case-control studies conducted during past foodborne disease outbreak investigations. We consider our survey an additional helpful tool that will allow comparison with food consumption data from case-patients obtained in exploratory, hypothesis-generating interviews early on in outbreak investigations, and which may assist in forming hypotheses regarding associations of illnesses with suspected food vehicles. This may facilitate and accelerate investigations of future foodborne disease outbreaks.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks.

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.

Comparison of 3-day and 7-day recall periods for food consumption reference values in foodborne disease outbreak investigations.

Investigations into an outbreak of foodborne disease attempt to identify the source of illness as quickly as possible. Population-based reference values for food consumption can assist in investigation by providing comparison data for hypothesis generation and also strengthening the evidence associated with a food product through hypothesis testing. In 2014-2015 a national phone survey was conducted in Canada to collect data on food consumption patterns using a 3- or 7-day recall period. The resulting food consumption values over the two recall periods were compared. The majority of food products did not show a significant difference in the consumption over 3 days and 7 days. However, comparison of reference values from the 3-day recall period to data from an investigation into a Salmonella Infantis outbreak was shown to support the conclusion that chicken was the source of the outbreak whereas the reference values from a 7-day recall did not support this finding. Reference values from multiple recall periods can assist in the hypothesis generation and hypothesis testing phase of foodborne outbreak investigations.

Incubation periods of enteric illnesses in foodborne outbreaks, United States, 1998-2013.

Early in a foodborne disease outbreak investigation, illness incubation periods can help focus case interviews, case definitions, clinical and environmental evaluations and predict an aetiology. Data describing incubation periods are limited. We examined foodborne disease outbreaks from laboratory-confirmed, single aetiology, enteric bacterial and viral pathogens reported to United States foodborne disease outbreak surveillance from 1998-2013. We grouped pathogens by clinical presentation and analysed the reported median incubation period among all illnesses from the implicated pathogen for each outbreak as the outbreak incubation period. Outbreaks from preformed bacterial toxins (Staphylococcus aureus, Bacillus cereus and Clostridium perfringens) had the shortest outbreak incubation periods (4-10 h medians), distinct from that of Vibrio parahaemolyticus (17 h median). Norovirus, salmonella and shigella had longer but similar outbreak incubation periods (32-45 h medians); campylobacter and Shiga toxin-producing Escherichia coli had the longest among bacteria (62-87 h medians); hepatitis A had the longest overall (672 h median). Our results can help guide diagnostic and investigative strategies early in an outbreak investigation to suggest or rule out specific etiologies or, when the pathogen is known, the likely timeframe for exposure. They also point to possible differences in pathogenesis among pathogens causing broadly similar syndromes.

The Foodborne Outbreak Challenge—Using Experiential Learning to Foster Interdisciplinary Training Among Students on Foodborne Disease Outbreak Investigations

AbstractThe Integrated Food Safety Centers of Excellence aim to develop novel learning methods to educate and train the future public health workforce to investigate foodborne outbreaks. The Foodborne Outbreak Challenge (FOC) was a one‐day event hosted by the Colorado Integrated Food Safety Center of Excellence at the Colorado School of Public Health. The FOC incorporated experiential, problem‐based, and interdisciplinary pedagogies from case studies, simulation exercises, and public health case competitions, to deliver a novel learning experience that met the training needs of a multidisciplinary foodborne outbreak response team with diverse skills sets. The event received positive feedback, and participants demonstrated knowledge gain. Event materials are available for other institutions to use.

A foodborne disease outbreak investigation experience in a College in Lusaka, Zambia, 2017

Introduction : On 19 March 2017, an outbreak of unknown etiology was reported among students at a college in Lusaka, Zambia. We investigated to confirm the outbreak, identify exposures, determine the aetiological agent, and implement preventive measures. Methods : We conducted an unmatched case-control study. Cases and controls were selected conveniently. A suspected case was diarrhea or abdominal pains in any student at College A and Controls were asymptomatic students at College A during 18-23 March. We interviewed cases and controls about exposures to suspected food and water and collected saved food samples and swabs from food-handlers' hands and kitchen surfaces for culture. We analyzed data using Epi-info v 7.2 (Atlanta, Georgia). Results : We identified 59 suspected case-patients. Predominant symptoms were diarrhea (n = 51.83%) and abdominal pains (n = 44.75%). The outbreak started on 18 March, peaked on 19, and concluded on 20 March. We interviewed 30 case-patients and 71 controls. Exposures associated with increased odds of illness included eating food served at dinner on Saturday (18 March) in school cafeteria (OR = 5.8, 95% CI = 2.0-16.7); specifically, eating beans at Saturday dinner (OR = 21.6, 95% CI = 4.5- 104) and drinking water supplied at school (OR = 8.8, 95% CI = 1.45-53.6). Samples from all food-handlers (n = 13) yielded Staphylococcus aureus and all food samples (n = 3) yielded Escherichia coli, Staphylococcus aureus and fecal coliforms. Conclusion : The results suggest a foodborne outbreak caused by consumption of contaminated food served at dinner on 18 March at College A. We educated the food handlers and school management about the importance of disinfection of preparation surfaces, supervision of food handling and handwashing practices.

Simultaneous Detection of Multiple Salmonella Serovars from Milk and Chicken Meat by Real-Time PCR Using Unique Genomic Target Regions

A novel genomic and plasmid target-based PCR platform was developed for the detection of Salmonella serovars Heidelberg, Dublin, Hadar, Kentucky, and Enteritidis. Unique genome loci were obtained through extensive genome mining of protein databases and comparative genomic analysis of these serovars. Assays targeting Salmonella serovars Hadar, Heidelberg, Kentucky, and Dublin had 100% specificity and sensitivity, whereas those for Salmonella Enteritidis had 97% specificity and 88% sensitivity. The limits of detection for Salmonella serovars Heidelberg, Kentucky, Hadar, Enteritidis, and Dublin were 12, 9, 40, 13, and 5,280 CFU, respectively. A sensitivity assay was also performed by using milk artificially inoculated with pooled Salmonella serovars, yielding a detection limit of 1 to10 CFU/25 mL of milk samples after enrichment. The minimum DNA detected using the multiplexed TaqMan assay was 75.8 fg (1.53 × 101 genomic equivalents [GE]) for Salmonella Heidelberg, 140.8 fg (2.8 × 101 GE) for Salmonella Enteritidis, and 3.48 pg (6.96 × 102 GE) for Salmonella Dublin. PCR efficiencies were 89.8% for Salmonella Heidelberg, 94.5% for Salmonella Enteritidis, and 75.5% for Salmonella Dublin. Four types of 30 pasteurized milk samples were tested negative by culture techniques and with a genus-specific Salmonella invA gene PCR assay. Among 30 chicken samples similarly tested, 12 (40%) were positive by both culture and the invA PCR. Testing of these 12 samples with the serovar-specific PCR assay detected single and mixed contamination with Salmonella Kentucky, Salmonella Enteritidis, and Salmonella Heidelberg. Five unique primers were designed and tested by multiplex conventional PCR in conjunction with the use of the multiplex TaqMan assay with three of the primers. The diagnostic assays developed in this study could be used as tools for routine detection of these five Salmonella serovars and for epidemiological investigations of foodborne disease outbreaks.

FoodChain-Lab: A Trace-Back and Trace-Forward Tool Developed and Applied during Food-Borne Disease Outbreak Investigations in Germany and Europe.

FoodChain-Lab is modular open-source software for trace-back and trace-forward analysis in food-borne disease outbreak investigations. Development of FoodChain-Lab has been driven by a need for appropriate software in several food-related outbreaks in Germany since 2011. The software allows integrated data management, data linkage, enrichment and visualization as well as interactive supply chain analyses. Identification of possible outbreak sources or vehicles is facilitated by calculation of tracing scores for food-handling stations (companies or persons) and food products under investigation. The software also supports consideration of station-specific cross-contamination, analysis of geographical relationships, and topological clustering of the tracing network structure. FoodChain-Lab has been applied successfully in previous outbreak investigations, for example during the 2011 EHEC outbreak and the 2013/14 European hepatitis A outbreak. The software is most useful in complex, multi-area outbreak investigations where epidemiological evidence may be insufficient to discriminate between multiple implicated food products. The automated analysis and visualization components would be of greater value if trading information on food ingredients and compound products was more easily available.

FoodChain-lab: Tracing Software Supporting Foodborne Disease Outbreak Investigations

In case of foodborne disease outbreaks, rapid identification of the causative food product is essential, since the medical and economic damages grow with the duration of the outbreak. Therefore and based on the experiences from recent foodborne disease outbreaks a free, open source software called FoodChain-Lab has been developed. It evolved from a supply chain data visualization and analysis tool into a comprehensive toolbox for data management, data enrichment, visualization, data analysis and interactive reasoning.FoodChain-Lab is applicable in food- or feed-borne disease outbreak investigations as well as in exposure assessment tasks related to feed or food supply chains.

Development of an ERIC sequence typing scheme for Laribacter hongkongensis, an emerging pathogen associated with community-acquired gastroenteritis and travellers’ diarrhoea

Laribacter hongkongensis is a potential emerging pathogen, associated with community-acquired diarrhoea. For epidemiological purposes, different molecular typing methods, such as pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing, have been developed for this pathogen. However, these methods require specialized equipment and costly reagents. More importantly, they are labour-intensive and time-consuming, which is not really suitable for foodborne disease outbreak investigations. In this study, we developed a rapid and reliable method using 22-mer primers specific for the enterobacterial repetitive intergenic consensus sequence (ERIC). PFGE was used for comparison, to evaluate this method. A total of 81 isolates of L. hongkongensis were examined: 79 isolates recovered from food of diverse origins and two strains derived from patients with L. hongkongensis-associated infection. Typing patterns and clustering analysis indicated that the 81 L. hongkongensis isolates were grouped into 21 and 13 genotypes by ERIC-PCR and PFGE, respectively. ERIC-PCR was found as reproducible as PFGE. A high percentage (70.4%) of isolates yielded distinguishable ERIC-PCR patterns, which were concordant with the results from PFGE. These results suggest that ERIC-PCR is valuable for use in the epidemiological investigation of L. hongkongensis.

Diagnostic et contrôle en médecine humaine des toxiinfections alimentaires collectives

Medical microbiology laboratories play a key role in the investigation of foodborne disease outbreaks. Bacterial pathogens (Salmonella, Escherichia coli, Campylobacter, Staphylococcus aureus, Bacillus cereus, Clostridium perfringens, etc) have historically been implicated in foodborne illness, while the role of viruses (especially Norovirus) appears to have been underestimated. Culture-based diagnosis has gradually been complemented, or replaced, by rapid molecular methods applied directly to biological samples. These new tools should help to reduce the number of outbreaks in which the etiological agent goes unidentified, and to improve the exhaustiveness of notifications.