Browsing by Author "Lopez-Chavarrias, Vicente"
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- Building an International One Health Strain Level Database to Characterise the Epidemiology of AMR Threats: ESBL—AmpC Producing E. coli as An Example—Challenges and PerspectivesPublication . Perestrelo, Sara; Amaro, Ana; Brouwer, Michael S.M.; Clemente, Lurdes; Ribeiro Duarte, Ana Sofia; Kaesbohrer, Annemarie; Karpíšková, Renata; Lopez-Chavarrias, Vicente; Morris, Dearbháile; Prendergast, Deirdre; Pista, Angela; Silveira, Leonor; Skarżyńska, Magdalena; Slowey, Rosemarie; Veldman, Kees T.; Zając, Magdalena; Burgess, Catherine; Alvarez, JulioAntimicrobial resistance (AMR) is one of the top public health threats nowadays. Among the most important AMR pathogens, Escherichia coli resistant to extended spectrum cephalosporins (ESC-EC) is a perfect example of the One Health problem due to its global distribution in animal, human, and environmental sources and its resistant phenotype, derived from the carriage of plasmid-borne extended-spectrum and AmpC β-lactamases, which limits the choice of effective antimicrobial therapies. The epidemiology of ESC-EC infection is complex as a result of the multiple possible sources involved in its transmission, and its study would require databases ideally comprising information from animal (livestock, companion, wildlife), human, and environmental sources. Here, we present the steps taken to assemble a database with phenotypic and genetic information on 10,763 ESC-EC isolates retrieved from multiple sources provided by 13 partners located in eight European countries, in the frame of the DiSCoVeR Joint Research project funded by the One Health European Joint Programme (OH-EJP), along with its strengths and limitations. This database represents a first step to help in the assessment of different geographical and temporal trends and transmission dynamics in animals and humans. The work performed highlights aspects that should be considered in future international efforts, such as the one presented here.
- Source attribution of human 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, Vicente; Rosendal, Thomas; Clemente, Lurdes; Amaro, Ana; Zomer, Aldert L.; 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 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 and . 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 -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.