Use of wild bird surveillance, human case data and GIS spatial analysis for predicting spatial distributions of West Nile Virus in Greece

Khudyakov, Yury E.; Valiakos, George; Papaspyropoulos, Konstantinos; Giannakopoulos, Alexios; Birtsas, Periklis; Tsiodras, Sotirios; Hutchings, Michael R.; Spyrou, Vassiliki; Pervanidou, Danai; Athanasiou, Labrini V.; Papadopoulos, Nikolaos; Tsokana, Constantina; Baka, Agoritsa; Manolakou, Katerina; Chatzopoulos, Dimitrios; Artois, Marc; Yon, Lisa; Hannant, Duncan; Petrovska, Liljana; Hadjichristodoulou, Christos; Billinis, Charalambos

Abstract

West Nile Virus (WNV) is the causative agent of a vector-borne, zoonotic disease with a worldwide distribution. Recent expansion and introduction of WNV into new areas, including southern Europe, has been associated with severe disease in humans and equids, and has increased concerns regarding the need to prevent and control future WNV outbreaks. Since 2010, 524 confirmed human cases of the disease have been reported in Greece with greater than 10% mortality. Infected mosquitoes, wild birds, equids, and chickens have been detected and associated with human disease. The aim of our study was to establish a monitoring system with wild birds and reported human cases data using Geographical Information System (GIS). Potential distribution of WNV was modelled by combining wild bird serological surveillance data with environmental factors (e.g. elevation, slope, land use, vegetation density, temperature, precipitation indices, and population density). Local factors including areas of low altitude and proximity to water were important predictors of appearance of both human and wild bird cases (Odds Ratio = 1,001 95%CI = 0,723–1,386). Using GIS analysis, the identified risk factors were applied across Greece identifying the northern part of Greece (Macedonia, Thrace) western Greece and a number of Greek islands as being at highest risk of future outbreaks. The results of the analysis were evaluated and confirmed using the 161 reported human cases of the 2012 outbreak predicting correctly (Odds = 130/31 = 4,194 95%CI = 2,841–6,189) and more areas were identified for potential dispersion in the following years. Our approach verified that WNV risk can be modelled in a fast cost-effective way indicating high risk areas where prevention measures should be implemented in order to reduce the disease incidence.

Citation

Khudyakov, Y. E., Valiakos, G., Papaspyropoulos, K., Giannakopoulos, A., Birtsas, P., Tsiodras, S., …Billinis, C. (2014). Use of wild bird surveillance, human case data and GIS spatial analysis for predicting spatial distributions of West Nile Virus in Greece. PLoS ONE, 9(5), Article e96935. https://doi.org/10.1371/journal.pone.0096935

• Journal Article Type: Article
• Acceptance Date: Apr 11, 2014
• Publication Date: May 7, 2014
• Deposit Date: Oct 18, 2016
• Publicly Available Date: Oct 18, 2016
• Journal: PLoS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 5
• Article Number: e96935
• DOI: https://doi.org/10.1371/journal.pone.0096935
• Public URL: https://nottingham-repository.worktribe.com/output/729104
• Publisher URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096935