| Literature DB >> 27590848 |
Alexandra Chaskopoulou1, Gregory L'Ambert2, Dusan Petric3, Romeo Bellini4, Marija Zgomba3, Thomas A Groen5, Laurence Marrama6, Dominique J Bicout7,8.
Abstract
West Nile virus (WNV) represents a serious burden to human and animal health because of its capacity to cause unforeseen and large epidemics. Until 2004, only lineage 1 and 3 WNV strains had been found in Europe. Lineage 2 strains were initially isolated in 2004 (Hungary) and in 2008 (Austria) and for the first time caused a major WNV epidemic in 2010 in Greece with 262 clinical human cases and 35 fatalities. Since then, WNV lineage 2 outbreaks have been reported in several European countries including Italy, Serbia and Greece. Understanding the interaction of ecological factors that affect WNV transmission is crucial for preventing or decreasing the impact of future epidemics. The synchronous co-occurrence of competent mosquito vectors, virus, bird reservoir hosts, and susceptible humans is necessary for the initiation and propagation of an epidemic. Weather is the key abiotic factor influencing the life-cycles of the mosquito vector, the virus, the reservoir hosts and the interactions between them. The purpose of this paper is to review and compare mosquito population dynamics, and weather conditions, in three ecologically different contexts (urban/semi-urban, rural/agricultural, natural) across four European countries (Italy, France, Serbia, Greece) with a history of WNV outbreaks. Local control strategies will be described as well. Improving our understanding of WNV ecology is a prerequisite step for appraising and optimizing vector control strategies in Europe with the ultimate goal to minimize the probability of WNV infection.Entities:
Keywords: Control; Ecology; Europe; Modelling; West Nile fever; West Nile virus
Mesh:
Year: 2016 PMID: 27590848 PMCID: PMC5009705 DOI: 10.1186/s13071-016-1736-6
Source DB: PubMed Journal: Parasit Vectors ISSN: 1756-3305 Impact factor: 3.876
Fig. 1Italian West Nile virus functional unit. a Culex surveillance system with CDC traps and landscape management by CORINE. b Cx. pipiens population dynamics and weather data (6 years average)
Fig. 2French WNV functional unit. a Culex surveillance system with CDC traps and landscape management by CORINE. b Cx. pipiens population dynamics and weather data (4 years average)
Fig. 3Serbian WNV functional unit. a Culex surveillance system with NS2 traps and landscape management by CORINE. b Cx. pipiens population dynamics and weather data in the urban zone (8 years average). c Cx. pipiens population dynamics and weather data in the rural zone (8 years average). d Cx. pipiens population dynamics and weather data in the semi-urban zone (8 years average)
Fig. 4Greek WNV functional unit. a Culex surveillance system with CDC traps and land scape management by CORINE. b Cx. pipiens population dynamics and weather data in the rural/residential zone (4 years average). c Cx. pipiens population dynamics and weather data in the rice fields (4 years average)
Fig. 5Summary of the lagged cross-correlation analysis between study sites for temperature, precipitation, and Culex spp. population series. Numbers at the intersection between two countries corresponds to the highest Pearson cross-correlation value (2nd column) with the associated lag period (1st column). The lag units are months for temperature and precipitation and weeks for Culex spp. populations. The cross-correlation reads as: X [of the country site (in the row) at time t + lag] correlates with X [of the country site (in the column at the bottom row) at time t] with X = Temperature, Precipitation, Culex population