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Usability of the international HAVNet hepatitis A virus database for geographical annotation, backtracing and outbreak detection
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View Affiliations Hide AffiliationsAnnelies Kronemanannelies.kroneman rivm.nl
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Citation style for this article: . Usability of the international HAVNet hepatitis A virus database for geographical annotation, backtracing and outbreak detection. Euro Surveill. 2018;23(37):pii=1700802. https://doi.org/10.2807/1560-7917.ES.2018.23.37.1700802 Received: 27 Nov 2017; Accepted: 13 Apr 2018
Abstract
HAVNet is an international laboratory network sharing sequences and corresponding metadata on hepatitis A virus in an online database. Aim: We give an overview of the epidemiological and genetic data and assess the usability of the present dataset for geographical annotation, backtracing and outbreak detection. Methods: A descriptive analysis was performed on the timeliness, completeness, epidemiological data and geographic coverage of the dataset. Length and genomic region of the sequences were reviewed as well as the numerical and geographical distribution of the genotypes. The geographical signal in the sequences was assessed based on a short common nt stretch using a 100% identity analysis. Results: The 9,211 reports were heterogeneous for completeness and timeliness, and for length and genomic region of the sequences. Some parts of the world were not represented by the sequences. Geographical differences in prevalence of HAV genotypes described previously could be confirmed with this dataset and for a third (1,075/3,124) of the included sequences, 100% identity of the short common sequence coincided with an identical country of origin. Conclusion: Analysis of a subset of short, shared sequences indicates that a geographical annotation on the level of individual countries is possible with the HAVNet data. If the current incompleteness and heterogeneity of the data can be improved on, HAVNet could become very useful as a worldwide reference set for geographical annotation and for backtracing and outbreak detection.
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