Fig 1.
WNV laboratory confirmed cases among patients with fever or neuroinvasive disease in Russia and the Volgograd region, 1997–2019.
Source: official records of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) upon request.
Fig 2.
Spatial distribution of WNV laboratory confirmed cases among patients with fever or neuroinvasive disease per administrative units in Russia, 1997–2019.
Source: official records of Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) upon request. Contains information from OpenStreetMap and OpenStreetMap Foundation, which is made available under the Open Database License. The numbers indicate: 1 Adygea; 2 Astrakhan region; 3 Belgorod region; 4 Volgograd region; 5 Voronezh region; 6 Kaluga region; 7 Krasnodar krai; 8 Kursk region; 9 Lipetsk region; 10 Novosibirsk region; 11 Omsk region; 12 Kalmykia; 13 Rostov region; 14 Samara region; 15 Saratov region; 16 Stavropol krai; 17 Tatarstan; 18 Tula region; 19 Ulyanovsk region; 20 Chelyabinsk region.
Fig 3.
Study area–Volgograd city and its suburbs in southern Russia.
Contains information from OpenStreetMap and OpenStreetMap Foundation, which is made available under the Open Database License.
Table 1.
The statistical analysis of human WNV cases in the Volgograd region, 1997–2019.
Fig 4.
WNV detection sites (1996–2016) and possible places of human infection (2011) in the environment in Volgograd and neighbouring areas.
Contains information from OpenStreetMap and OpenStreetMap Foundation, which is made available under the Open Database License.
Table 2.
Response, explanatory, and offset variables for spatial modelling of heterogeneity of environmental conditions for WNV distribution.
Table 3.
The set of environmental predictors in different combinations for modelling.
Table 4.
Variable contributions according to various spatial models.
Fig 5.
Environmental suitability for WNV distribution.
(A) Model 1 based on the data on virus detection sites in the environment as presence data with natural environmental explanatory variables, (B) Model 2 based on the virus detection sites in the environment with natural and urban environmental explanatory variables, and (C) Model 3 based on the data on possible human infection and virus detection sites in the environment with natural and urban environmental explanatory variables. The colour indicates the degree of suitability.
Fig 6.
Response curves reflecting the influence of road density (A), building density (B) and LST (C) on the likelihood of the appearance of WNV distribution. The blue area shows the statistical significance of the response curve.
Fig 7.
Response curves reflecting the influence of distance to the water bodies (A), NDWI (B) and railway density (C) on the likelihood of the appearance of WNV distribution. The blue area shows the statistical significance of the response curve.
Fig 8.
Response curves reflecting the influence of elevation on the likelihood of the appearance of WNV distribution.
The blue area shows the statistical significance of the response curve.
Table 5.
Descriptive statistics and trend values for variables of thermal suitability and daily meteorological variables for WNV transmission between March and October from 1997 to 2019.
Fig 9.
Observed vs predicted number of WNF cases as per negative binomial regression results with the sum of ET as predictor.