| Autor: |
Cecilie Thystrup, Tine Hald, Dinaol Belina, Tesfaye Gobena |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2334 |
| DOI: |
10.1186/s12879-024-09800-4 |
| Popis: |
Abstract Background Foodborne diseases (FBDs) represent a significant risk to public health, with nearly one in ten people falling ill every year globally. The large incidence of foodborne diseases in African low- and middle-income countries (LMIC) shows the immediate need for action, but there is still far to a robust and efficient outbreak detection system. The detection of outbreak heavily relies on clinical diagnosis, which are often delayed or ignored due to resource limitations and inadequate surveillance systems. Methods In total, 68 samples of non-typhoidal Salmonella isolates from human, animal and environmental sources collected between November 2021 and January 2023 were analyzed using sequencing methods to infer phylogenetic relationships between the samples. A source attribution model using a machine-learning logit-boost that predicted the likely source of infection for 20 cases of human salmonellosis was also run and compared with the results of the cluster detection. Results Three clusters of samples with close relation (SNP difference |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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