Comparison of Source Attribution Methodologies for Human Campylobacteriosis
<i>Campylobacter</i> 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,...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Pathogens |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0817/12/6/786 |
| _version_ | 1797593134985641984 |
|---|---|
| author | Maja Lykke Brinch Tine Hald Lynda Wainaina Alessandra Merlotti Daniel Remondini Clementine Henri Patrick Murigu Kamau Njage |
| author_facet | Maja Lykke Brinch Tine Hald Lynda Wainaina Alessandra Merlotti Daniel Remondini Clementine Henri Patrick Murigu Kamau Njage |
| author_sort | Maja Lykke Brinch |
| collection | DOAJ |
| description | <i>Campylobacter</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78.99</mn><mo>%</mo></mrow></semantics></math></inline-formula> and an F1-score value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while the machine-learning algorithm showed the highest accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98</mn><mo>%</mo></mrow></semantics></math></inline-formula>). 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>65.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>, 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. |
| first_indexed | 2024-03-11T02:04:34Z |
| format | Article |
| id | doaj.art-b6b3e1966f2f4b25887b3dc3a617e7d1 |
| institution | Directory Open Access Journal |
| issn | 2076-0817 |
| language | English |
| last_indexed | 2024-03-11T02:04:34Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pathogens |
| spelling | doaj.art-b6b3e1966f2f4b25887b3dc3a617e7d12023-11-18T11:59:54ZengMDPI AGPathogens2076-08172023-05-0112678610.3390/pathogens12060786Comparison of Source Attribution Methodologies for Human CampylobacteriosisMaja Lykke Brinch0Tine Hald1Lynda Wainaina2Alessandra Merlotti3Daniel Remondini4Clementine Henri5Patrick Murigu Kamau Njage6Research Group for Foodborne Pathogens and Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkResearch Group for Foodborne Pathogens and Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkDepartment of Mathematics, University of Padova, 35121 Padova, ItalyDepartment of Physics and Astronomy, University of Bologna, 40126 Bologna, ItalyDepartment of Physics and Astronomy, University of Bologna, 40126 Bologna, ItalyResearch Group for Foodborne Pathogens and Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kongens Lyngby, DenmarkResearch Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark<i>Campylobacter</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>78.99</mn><mo>%</mo></mrow></semantics></math></inline-formula> and an F1-score value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>67</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while the machine-learning algorithm showed the highest accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98</mn><mo>%</mo></mrow></semantics></math></inline-formula>). 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>65.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>, 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.https://www.mdpi.com/2076-0817/12/6/786source attribution<i>Campylobacter</i>campylobacteriosisnetwork analysiswhole-genome sequencingcoherence source clustering |
| spellingShingle | Maja Lykke Brinch Tine Hald Lynda Wainaina Alessandra Merlotti Daniel Remondini Clementine Henri Patrick Murigu Kamau Njage Comparison of Source Attribution Methodologies for Human Campylobacteriosis Pathogens source attribution <i>Campylobacter</i> campylobacteriosis network analysis whole-genome sequencing coherence source clustering |
| title | Comparison of Source Attribution Methodologies for Human Campylobacteriosis |
| title_full | Comparison of Source Attribution Methodologies for Human Campylobacteriosis |
| title_fullStr | Comparison of Source Attribution Methodologies for Human Campylobacteriosis |
| title_full_unstemmed | Comparison of Source Attribution Methodologies for Human Campylobacteriosis |
| title_short | Comparison of Source Attribution Methodologies for Human Campylobacteriosis |
| title_sort | comparison of source attribution methodologies for human campylobacteriosis |
| topic | source attribution <i>Campylobacter</i> campylobacteriosis network analysis whole-genome sequencing coherence source clustering |
| url | https://www.mdpi.com/2076-0817/12/6/786 |
| work_keys_str_mv | AT majalykkebrinch comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT tinehald comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT lyndawainaina comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT alessandramerlotti comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT danielremondini comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT clementinehenri comparisonofsourceattributionmethodologiesforhumancampylobacteriosis AT patrickmurigukamaunjage comparisonofsourceattributionmethodologiesforhumancampylobacteriosis |