Percorrer por autor "Mughini-Gras, Lapo"
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- Attributable sources of the five most prevalent non-typhoidal Salmonella serovars across ten European countriesPublication . Teunis, Gijs; Dallman, Timothy J.; Zajac, Magdalena; Skarżyńska, Magdalena; Petrovska, Liljana; Pista, Ângela; Silveira, Leonor; Clemente, Lurdes; Thépault, Amandine; Bonifait, Laetitia; Kerouanton, Annaelle; Chemaly, Marianne; Alvarez, Julio; Soderlund, Robert; Nielsen, Eva Moller; Chattaway, Marie; Burgess, Kaye; Byrne, William; Zomer, Aldert L.; van den Beld, Maaike; Hendrickx, Antoni P.A.; Franz, Eelco; Pires, Sara; Hald, Tine; Mughini-Gras, LapoNon-typhoidal Salmonella is the second most frequently reported zoonotic pathogen in the European Union and European Economic Area. Most human infections are caused by serovars Enteritidis and Typhimurium. Genomic characterisation of Salmonella isolates from humans and animals has become a routine public health surveillance tool in many countries. In this study, the relative contributions of several potential sources of human infection of the five frequently reported Salmonella serovars were estimated using machine-learning methods based on a large, cross-sectional collection of genomes from human cases, and animal and environmental sources, across ten European countries. To define the population structure, core-genome Multilocus Sequence Typing was performed. A supervised machine-learning approach was applied for source attribution in the form of a Random Forest classifier. The source and country attribution models achieved moderate accuracy (F1=0.6–0.9), which is lower than in previous studies using machine-learning on Whole Genome Sequencing data. However, attributions of human clinical isolates to different sources were generally in line with previous findings for these five serovars. While the lack of clonality in some sources hindered their prediction, it is also likely that certain sources (e.g., pets) do not serve as major contributors to human infection. Therefore, in most cases attributing these sources to the livestock species they are typically associated with, is likely appropriate. Country attributions showed that substantial human cases are attributable to countries other than their own, indicating geographical interrelatedness of sources. This highlights the value of internationally harmonised Salmonella-control policies in the food production chain.
- Source attribution of human Campylobacter infection: a multi-country model in the European UnionPublication . Thystrup, Cecilie; Brinch, Maja Lykke; Henri, Clementine; Mughini-Gras, Lapo; Franz, Eelco; Wieczorek, Kinga; Gutierrez, Montserrat; Prendergast, Deirdre M.; Duffy, Geraldine; Burgess, Catherine M.; Bolton, Declan; Alvarez, Julio; Lopez-Chavarrias, Vicent; Rosendal, Thomas; Clemente, Lurdes; Amaro, Ana; Aldert L. Zomer; Joensen, Katrine Grimstrup; Nielsen, Eva Møller; Scavia, Gaia; Skarżyńska, Magdalena; Pinto, Miguel; Oleastro, Mónica; Cha, Wonhee; Thépault, Amandine; Rivoal, Katell; Denis, Martine; Chemaly, Marianne; Hald, TineIntroduction: Infections 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. Methods: We 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. Results: The 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%;). Discussion: The 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.