VBORNET Gap Analysis: Sand Fly Vector Distribution Models Utilised to Identify Areas of Potential Species Distribution in Areas Lacking Records. (19th December 2016)

• Record Type: Journal Article
• Authors: Alten, Bulent; Versteirt, Veerle; Van Bortel, Wim; Zeller, Herve; Wint, William; Alexander, Neil Stewart
• Is Part Of: Open health data. Volume 4(2016)
• Publication Date: 2016-12-19
• Subjects: VBORNET -- Sand flies -- species -- distribution -- habitat -- Non-Linear Discriminant Analysis -- Random Forest -- Generalised Linear Modelling; Medical informatics -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; 610.285
• DOI: 10.5334/ohd.26
• Languages: English
• ISSNs: 2054-7102

Abstract

This is the first of a number of planned data papers presenting modelled vector distributions, the models in this paper were produced during the ECDC funded VBORNET project. This work continues under the VectorNet project now jointly funded by ECDC and EFSA. This data paper contains the sand fly model outputs produced as part of the VBORNET project. Further data papers will be published after sampling seasons when more field data will become available allowing further species to be modelled or validation and updates to existing models. The data package described here includes those sand fly species first modelled in 2013 and 2014 as part of the VBORNET gap analysis work which aimed to identify areas of potential species distribution in areas lacking records. It comprises four species models together with suitability masks based on land class and environmental limits. The species included within this paper are Phlebotomus ariasi, Phlebotomus papatasi, Phlebotomus perniciosus and Phlebotomus tobbi . The known distributions of these species within the project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) assistance in targeting surveys to collect distribution data for those areas with no field validated information, and b) a first indication of project wide distributions.