Identification of Climatic Factors Affecting the Epidemiology of Human West Nile Virus Infections in Northern Greece

Abstract

Climate can affect the geographic and seasonal patterns of vector-borne disease incidence such as West Nile Virus (WNV) infections. We explore the association between climatic factors and the occurrence of West Nile fever (WNF) or West Nile neuro-invasive disease (WNND) in humans in Northern Greece over the years 2010–2014. Time series over a period of 30 years (1979–2008) of climatic data of air temperature, relative humidity, soil temperature, volumetric soil water content, wind speed, and precipitation representing average climate were obtained utilising the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-Interim) system allowing for a homogeneous set of data in time and space. We analysed data of reported human cases of WNF/WNND and Culex mosquitoes in Northern Greece. Quantitative assessment resulted in identifying associations between the above climatic variables and reported human cases of WNF/WNND. A substantial fraction of the cases was linked to the upper percentiles of the distribution of air and soil temperature for the period 1979–2008 and the lower percentiles of relative humidity and soil water content. A statistically relevant relationship between the mean weekly value climatic anomalies of wind speed (negative association), relative humidity (negative association) and air temperature (positive association) over 30 years, and reported human cases of WNF/WNND during the period 2010–2014 could be shown. A negative association between the presence of WNV infected Culex mosquitoes and wind speed could be identified. The statistically significant associations could also be confirmed for the week the WNF/WNND human cases appear and when a time lag of up to three weeks was considered. Similar statistically significant associations were identified with the weekly anomalies of the maximum and minimum values of the above climatic factors. Utilising the ERA-Interim re-analysis methodology it could be shown that besides air temperature, climatic factors such as soil temperature, relative humidity, soil water content and wind speed may affect the epidemiology of WNV.

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

“…Higher dengue incidences were related to the MSL maxima due to the fact that coastal areas are more prone to flooding during seasonal peak. Stilianakis et al examined the soil water content as a parameter [30], which did not show an association with the presence of infected mosquitoes. Most of the studies (n = 16) referred to the role of the water bodies in the life circle of the mosquitoes because they serve as breeding sites for larval development.…”

“…Figure 2 illustrates the number and geographic reference of the selected studies together with the disease of study. The selected articles were organized into two main categories ( Figure 3) with respect to the data used as dependent variables for the prevalence of the diseases: (a) epidemiological data (disease incidence, prevalence or case, mortality data) (n = 31) and (b) entomological data (n = 11), while Stilianakis et al has examined both (a) and (b) [30], and Valiakos et al has additionally used wild bird data in complement to the epidemiological data [31]. The first category (a) used clinical records from the general human population as the main data source.…”

“…Associating LST and air temperature is a complex task as proved in [57] since it is highly dependent on the geographic location of the study area. Only one study [30] reported the association of WNV infections to soil temperatures obtained from the ECMWF's (European Centre for Medium-RangeWeather Forecasts)Re-Analysis (ERA-Interim) datasets. Soil temperature data refer to ground based observation at several depths [58].…”

“…The same holds for the Aedes genus, as their reproductive cycle can be disrupted by extensive rainfall through flushing out the aquatic stages from breeding sites [62]. Precipitation satellite sensor derived data were mainly acquired from the Tropical Rainfall Measuring Mission (TRMM)(n = 10 [32,35,40,43,50,51,[63][64][65][66][67]) (n = 10), while some studies used the WorldClim (https://www.worldclim.org/) (n = 3 [31,41,61]), ERA-Interim (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) (n = 1 [30]) and Meteosat-7 (n = 1 [45]) and other sources such as local ground-stations.…”

“…Relative humidity plays an important role in the survival rate of the vectors, affecting differently various species [72]. Relative humidity estimates were derived from EO sensors systems onboard of satellites such as the Indian National Satellite System (INSAT)-3D imager [63], the Atmospheric Infrared Sounder (AIRS) instrument onboard the NASA's Aqua satellite [43] and the ERA-Interim reanalysis approach by Stilianakis et al [30]. Adde et al estimated the minimum and maximum relative humidity [48], while in Machault et al the relative humidity was calculated using only in situ observations [44].…”