Anomaly detection in the veterinary antibiotic prescription surveillance system (IS ABV).

Antibiotic resistance is one of the major concerns in veterinary and human medicine and poses a considerable threat to both human and animal health. It has been shown that over- or misuse of antibiotics is one of the primary drivers of antibiotic resistance. To develop the surveillance of antibiotic use, Switzerland introduced the "Informationssystem Antibiotika in der Veterinärmedizin" (IS ABV) in 2019, mandating electronic registration of antibiotic prescriptions by all veterinarians in Switzerland. However, initial data analysis revealed a considerable amount of implausible data entries, potentially compromising data quality and reliability. These anomalies may be caused by input errors, inaccuracies, incorrect or aberrant master data or data transmission and make analysis impossible. To address this issue efficiently, we propose a two-stage anomaly detection framework utilizing machine learning algorithms. In this study, our primary focus was on cattle treatments with either single or group therapy, as they were the species with the highest prescription volume. However, not all outliers are necessarily incorrect; some may be legitimate but unusual antibiotic treatments. Thus, expert review plays a crucial role in distinguishing outliers, that are correct from actual errors. Initially, relevant prescription variables were extracted and pre-processed with a custom-built scaler. A set of unsupervised algorithms calculated the probability of each data point and identified the most likely outliers. In collaboration with experts, we annotated anomalies and established anomaly thresholds for each production type and active substance. These expert-annotated labels were then used to fine-tune the final supervised classification algorithms. With this methodology, we identified 22,816 anomalies from a total of 1,994,170 prescriptions in cattle (1.1 %). Cattle with no further specified production type had the most (2 %) anomalies with 7758 out of 379,995. The anomalies were consistently identified and comprised prescriptions with too high and too low dosages. Random Forest achieved a ROC-AUC score of 0.994, (95 % CI: 0.992, 0.995) and a F1-Score of 0.962 (95 % CI: 0.958, 0.966) for single treatments. The versatility of this framework allows its adaptation to other species within IS ABV and potentially to other prescription-based surveillance systems. If applied regularly to uploaded prescriptions, it should reduce input errors over time, improving the validity of the data in the long term.

Identification of a novel hantavirus strain in the root vole (Microtus oeconomus) in Lithuania, Eastern Europe.

Hantaviruses are zoonotic pathogens that can cause subclinical to lethal infections in humans. In Europe, five orthohantaviruses are present in rodents: Myodes-associated Puumala orthohantavirus (PUUV), Microtus-associated Tula orthohantavirus, Traemmersee hantavirus (TRAV)/ Tatenale hantavirus (TATV)/ Kielder hantavirus, rat-borne Seoul orthohantavirus, and Apodemus-associated Dobrava-Belgrade orthohantavirus (DOBV). Human PUUV and DOBV infections were detected previously in Lithuania, but the presence of Microtus-associated hantaviruses is not known. For this study we screened 234 Microtus voles, including root voles (Microtus oeconomus), field voles (Microtus agrestis) and common voles (Microtus arvalis) from Lithuania for hantavirus infections. This initial screening was based on reverse transcription-polymerase chain reaction (RT-PCR) targeting the S segment and serological analysis. A novel hantavirus was detected in eight of 79 root voles tentatively named "Rusne virus" according to the capture location and complete genome sequences were determined. In the coding regions of all three genome segments, Rusne virus showed high sequence similarity to TRAV and TATV and clustered with Kielder hantavirus in phylogenetic analyses of partial S and L segment sequences. Pairwise evolutionary distance analysis confirmed Rusne virus as a strain of the species TRAV/TATV. Moreover, we synthesized the entire nucleocapsid (N) protein of Rusne virus in Saccharomyces cerevisiae. We observed cross-reactivity of antibodies raised against other hantaviruses, including PUUV, with this new N protein. ELISA investigation of all 234 voles detected Rusne virus-reactive antibodies exclusively in four of 79 root voles, all being also RNA positive, but not in any other vole species. In conclusion, the detection of Rusne virus RNA in multiple root voles at the same trapping site during three years and its absence in sympatric field voles suggests root voles as the reservoir host of this novel virus. Future investigations should evaluate host association of TRAV, TATV, Kielder virus and the novel Rusne virus and their evolutionary relationships.