Source attribution of human Campylobacter infection: a multi-country model in the European Union
IntroductionInfections caused by Campylobacter spp. represent a severe threat to public health worldwide. National action plans have included source attribution studies as a way to quantify the contribution of specific sources and understand the dynamic of transmission of foodborne pathogens like Sa...
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Frontiers Media S.A.
2025-02-01
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| author | Cecilie Thystrup Maja Lykke Brinch Clementine Henri Lapo Mughini-Gras Lapo Mughini-Gras Eelco Franz Kinga Wieczorek Montserrat Gutierrez Deirdre M. Prendergast Geraldine Duffy Catherine M. Burgess Declan Bolton Julio Alvarez Julio Alvarez Vicente Lopez-Chavarrias Thomas Rosendal Lurdes Clemente Ana Amaro Aldert L. Zomer Katrine Grimstrup Joensen Eva Møller Nielsen Gaia Scavia Magdalena Skarżyńska Miguel Pinto Mónica Oleastro Wonhee Cha Amandine Thépault Katell Rivoal Martine Denis Marianne Chemaly Tine Hald |
| author_facet | Cecilie Thystrup Maja Lykke Brinch Clementine Henri Lapo Mughini-Gras Lapo Mughini-Gras Eelco Franz Kinga Wieczorek Montserrat Gutierrez Deirdre M. Prendergast Geraldine Duffy Catherine M. Burgess Declan Bolton Julio Alvarez Julio Alvarez Vicente Lopez-Chavarrias Thomas Rosendal Lurdes Clemente Ana Amaro Aldert L. Zomer Katrine Grimstrup Joensen Eva Møller Nielsen Gaia Scavia Magdalena Skarżyńska Miguel Pinto Mónica Oleastro Wonhee Cha Amandine Thépault Katell Rivoal Martine Denis Marianne Chemaly Tine Hald |
| author_sort | Cecilie Thystrup |
| collection | DOAJ |
| description | IntroductionInfections caused by Campylobacter spp. represent a severe threat to public health worldwide. National action plans have included source attribution studies as a way to quantify the contribution of specific sources and understand the dynamic of transmission of foodborne pathogens like Salmonella and Campylobacter. Such information is crucial for implementing targeted intervention. The aim of this study was to predict the sources of human campylobacteriosis cases across multiple countries using available whole-genome sequencing (WGS) data and explore the impact of data availability and sample size distribution in a multi-country source attribution model.MethodsWe constructed a machine-learning model using k-mer frequency patterns as input data to predict human campylobacteriosis cases per source. We then constructed a multi-country model based on data from all countries. Results using different sampling strategies were compared to assess the impact of unbalanced datasets on the prediction of the cases.ResultsThe results showed that the variety of sources sampled and the quantity of samples from each source impacted the performance of the model. Most cases were attributed to broilers or cattle for the individual and multi-country models. The proportion of cases that could be attributed with 70% probability to a source decreased when using the down-sampled data set (535 vs. 273 of 2627 cases). The baseline model showed a higher sensitivity compared to the down-sampled model, where samples per source were more evenly distributed. The proportion of cases attributed to non-domestic source was higher but varied depending on the sampling strategy. Both models showed that most cases could be attributed to domestic sources in each country (baseline: 248/273 cases, 91%; down-sampled: 361/535 cases, 67%;).DiscussionThe sample sizes per source and the variety of sources included in the model influence the accuracy of the model and consequently the uncertainty of the predicted estimates. The attribution estimates for sources with a high number of samples available tend to be overestimated, whereas the estimates for source with only a few samples tend to be underestimated. Reccomendations for future sampling strategies include to aim for a more balanced sample distribution to improve the overall accuracy and utility of source attribution efforts. |
| format | Article |
| id | doaj-art-0a73a109105941199cedd6ac01e30423 |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Microbiology |
| spelling | doaj-art-0a73a109105941199cedd6ac01e304232025-02-05T07:32:42ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-02-011610.3389/fmicb.2025.15191891519189Source attribution of human Campylobacter infection: a multi-country model in the European UnionCecilie Thystrup0Maja Lykke Brinch1Clementine Henri2Lapo Mughini-Gras3Lapo Mughini-Gras4Eelco Franz5Kinga Wieczorek6Montserrat Gutierrez7Deirdre M. Prendergast8Geraldine Duffy9Catherine M. Burgess10Declan Bolton11Julio Alvarez12Julio Alvarez13Vicente Lopez-Chavarrias14Thomas Rosendal15Lurdes Clemente16Ana Amaro17Aldert L. Zomer18Katrine Grimstrup Joensen19Eva Møller Nielsen20Gaia Scavia21Magdalena Skarżyńska22Miguel Pinto23Mónica Oleastro24Wonhee Cha25Amandine Thépault26Katell Rivoal27Martine Denis28Marianne Chemaly29Tine Hald30National Food Institute, Technical University of Denmark, Lyngby, DenmarkNational Food Institute, Technical University of Denmark, Lyngby, DenmarkNational Food Institute, Technical University of Denmark, Lyngby, DenmarkNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsInstitute for Risk Assessment Sciences, Utrecht University, Utrecht, NetherlandsNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsDepartment of Food Safety, NVRI, Pulawy, PolandFood Microbiology, Department of Agriculture, Food and the Marine, Celbridge, IrelandFood Microbiology, Department of Agriculture, Food and the Marine, Celbridge, IrelandTeagasc Food Research Centre, Dublin, IrelandTeagasc Food Research Centre, Dublin, IrelandTeagasc Food Research Centre, Dublin, IrelandVISAVET Health Surveillance Center, Universidad Complutense, Madrid, SpainAnimal Health Department, Faculty of Veterinary, Universidad Complutense, Madrid, SpainVISAVET Health Surveillance Center, Universidad Complutense, Madrid, SpainEpidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency, Uppsala, Sweden0National Institute of Agrarian and Veterinary Research, (INIAV), Oeiras, Portugal0National Institute of Agrarian and Veterinary Research, (INIAV), Oeiras, Portugal1Division of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands2Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark2Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark3Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanitá, Rome, Italy4Department of Microbiology, National Veterinary Research Institute (PIWet), Pulawy, Poland5Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, Portugal5Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), Lisbon, PortugalEpidemiology, Surveillance and Risk Assessment, Swedish Veterinary Agency, Uppsala, Sweden6Unit of Hygiene and Quality of Poultry and Pork Products, Laboratory of Ploufragan-Plouzané-Niort, French Agency for Food Environmental and Occupational Health and Safety (ANSES), Ploufragan, France6Unit of Hygiene and Quality of Poultry and Pork Products, Laboratory of Ploufragan-Plouzané-Niort, French Agency for Food Environmental and Occupational Health and Safety (ANSES), Ploufragan, France6Unit of Hygiene and Quality of Poultry and Pork Products, Laboratory of Ploufragan-Plouzané-Niort, French Agency for Food Environmental and Occupational Health and Safety (ANSES), Ploufragan, France6Unit of Hygiene and Quality of Poultry and Pork Products, Laboratory of Ploufragan-Plouzané-Niort, French Agency for Food Environmental and Occupational Health and Safety (ANSES), Ploufragan, FranceNational Food Institute, Technical University of Denmark, Lyngby, DenmarkIntroductionInfections caused by Campylobacter spp. represent a severe threat to public health worldwide. National action plans have included source attribution studies as a way to quantify the contribution of specific sources and understand the dynamic of transmission of foodborne pathogens like Salmonella and Campylobacter. Such information is crucial for implementing targeted intervention. The aim of this study was to predict the sources of human campylobacteriosis cases across multiple countries using available whole-genome sequencing (WGS) data and explore the impact of data availability and sample size distribution in a multi-country source attribution model.MethodsWe constructed a machine-learning model using k-mer frequency patterns as input data to predict human campylobacteriosis cases per source. We then constructed a multi-country model based on data from all countries. Results using different sampling strategies were compared to assess the impact of unbalanced datasets on the prediction of the cases.ResultsThe results showed that the variety of sources sampled and the quantity of samples from each source impacted the performance of the model. Most cases were attributed to broilers or cattle for the individual and multi-country models. The proportion of cases that could be attributed with 70% probability to a source decreased when using the down-sampled data set (535 vs. 273 of 2627 cases). The baseline model showed a higher sensitivity compared to the down-sampled model, where samples per source were more evenly distributed. The proportion of cases attributed to non-domestic source was higher but varied depending on the sampling strategy. Both models showed that most cases could be attributed to domestic sources in each country (baseline: 248/273 cases, 91%; down-sampled: 361/535 cases, 67%;).DiscussionThe sample sizes per source and the variety of sources included in the model influence the accuracy of the model and consequently the uncertainty of the predicted estimates. The attribution estimates for sources with a high number of samples available tend to be overestimated, whereas the estimates for source with only a few samples tend to be underestimated. Reccomendations for future sampling strategies include to aim for a more balanced sample distribution to improve the overall accuracy and utility of source attribution efforts.https://www.frontiersin.org/articles/10.3389/fmicb.2025.1519189/fullsource attributionfoodborne diseasecampylobacteriosismachine learningEuropean union |
| spellingShingle | Cecilie Thystrup Maja Lykke Brinch Clementine Henri Lapo Mughini-Gras Lapo Mughini-Gras Eelco Franz Kinga Wieczorek Montserrat Gutierrez Deirdre M. Prendergast Geraldine Duffy Catherine M. Burgess Declan Bolton Julio Alvarez Julio Alvarez Vicente Lopez-Chavarrias Thomas Rosendal Lurdes Clemente Ana Amaro Aldert L. Zomer Katrine Grimstrup Joensen Eva Møller Nielsen Gaia Scavia Magdalena Skarżyńska Miguel Pinto Mónica Oleastro Wonhee Cha Amandine Thépault Katell Rivoal Martine Denis Marianne Chemaly Tine Hald Source attribution of human Campylobacter infection: a multi-country model in the European Union Frontiers in Microbiology source attribution foodborne disease campylobacteriosis machine learning European union |
| title | Source attribution of human Campylobacter infection: a multi-country model in the European Union |
| title_full | Source attribution of human Campylobacter infection: a multi-country model in the European Union |
| title_fullStr | Source attribution of human Campylobacter infection: a multi-country model in the European Union |
| title_full_unstemmed | Source attribution of human Campylobacter infection: a multi-country model in the European Union |
| title_short | Source attribution of human Campylobacter infection: a multi-country model in the European Union |
| title_sort | source attribution of human campylobacter infection a multi country model in the european union |
| topic | source attribution foodborne disease campylobacteriosis machine learning European union |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1519189/full |
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