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. 2022 Jan 31;16(1):e0010145.
doi: 10.1371/journal.pntd.0010145. eCollection 2022 Jan.

Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci

Natalia Shartova  1 , Varvara Mironova  1 , Svetlana Zelikhina  1 , Fedor Korennoy  2 , Mikhail Grishchenko  1   3
Affiliations

Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci

Natalia Shartova et al. PLoS Negl Trop Dis. .

Abstract

Southern Russia remains affected by West Nile virus (WNV). In the current study, we identified the spatial determinants of WNV distribution in an area with endemic virus transmission, with special reference to the urban settings, by mapping probable points of human infection acquisition and points of virus detection in mosquitoes, ticks, birds, and mammals during 1999-2016. The suitability of thermal conditions for extrinsic virus replication was assessed based on the approach of degree-day summation and their changes were estimated by linear trend analysis. A generalized linear model was used to analyze the year-to-year variation of human cases versus thermal conditions. Environmental suitability was determined by ecological niche modelling using MaxEnt software. Human population density was used as an offset to correct for possible bias. Spatial analysis of virus detection in the environment showed significant contributions from surface temperature, altitude, and distance from water bodies. When indicators of location and mobility of the human population were included, the relative impact of factors changed, with roads becoming most important. When the points of probable human case infection were added, the percentage of leading factors changed only slightly. The urban environment significantly increased the epidemic potential of the territory and created quite favorable conditions for virus circulation. The private building sector with low-storey houses and garden plots located in the suburbs provided a connection between urban and rural transmission cycles.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
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
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
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.
Fig 4
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.
Fig 5
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
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
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
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.
Fig 9
Fig 9. Observed vs predicted number of WNF cases as per negative binomial regression results with the sum of ET as predictor.
See this image and copyright information in PMC

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Grants and funding

This research was funded by the Russian Science Foundation (Grant 17-77-20070 “Assessment and Forecast of the Bioclimatic Comfort of Russian Cities under Climate Change in the 21st Century”, N.V.S. awarded https://www.rscf.ru/en/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.