Outbreak detection in Harar town and Kersa district, Ethiopia using phylogenetic analysis and source attribution

Abstract

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 < 30) were identified as non-typhoidal Salmonella in Harar town and Kersa district, Ethiopia. These three clusters were comprised of isolates from different sources, including at least two human isolates. The isolates within each cluster showed identical serovar and sequence type (ST), with few exceptions in cluster 3. The close proximity of the samples suggested the occurrence of three potential outbreaks of non-typhoidal Salmonella in the region. The results of the source attribution model found that human cases of salmonellosis could primarily be attributed to bovine meat, which the results of the phylogenetic analysis corroborated. Conclusions The findings of this study suggested the occurrence of three possible outbreaks of non-typhoidal Salmonella in eastern Ethiopia, emphasizing the importance of targeted intervention of food safety protocols in LMICs. It also highlighted the potential of integrated surveillance for detecting outbreak and identifying the most probable source. Source attribution models in combination with other epidemiological methods is recommended as part of a more robust and integrated surveillance system for foodborne diseases.