Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

Olga I Zakharova; Fedor I Korennoy; Ivan V Iashin; Nadezhda N Toropova; Andrey E Gogin; Denis V Kolbasov; Galina V Surkova; Svetlana M Malkhazova; Andrei A Blokhin

doi:10.3389/fvets.2021.658675

Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

Front Vet Sci. 2021 Apr 12:8:658675. doi: 10.3389/fvets.2021.658675. eCollection 2021.

Authors

Olga I Zakharova¹, Fedor I Korennoy^{1

2}, Ivan V Iashin¹, Nadezhda N Toropova¹, Andrey E Gogin³, Denis V Kolbasov³, Galina V Surkova⁴, Svetlana M Malkhazova⁴, Andrei A Blokhin¹

Affiliations

¹ Federal Research Center for Virology and Microbiology, Nizhny Novgorod Research Veterinary Institute-Branch of Federal Research Center for Virology and Microbiology, Nizhny Novgorod, Russia.
² Federal Center for Animal Health (FGBI ARRIAH), Vladimir, Russia.
³ Federal Research Center for Virology and Microbiology, Pokrov, Russia.
⁴ Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia.

Abstract

Leptospirosis is a re-emerging zoonotic infectious disease caused by pathogenic bacteria of the genus Leptospira. Regional differences in the disease manifestation and the role of ecological factors, specifically in regions with a subarctic and arctic climate, remain poorly understood. We here explored environmental and socio-economic features associated with leptospirosis cases in livestock animals in the Russian Arctic during 2000-2019. Spatial analysis suggested that the locations of the majority of 808 cases were in "boreal" or "polar" climate regions, with "cropland," "forest," "shrubland," or "settlements" land-cover type, with a predominance of "Polar Moist Cropland on Plain" ecosystem. The cases demonstrated seasonality, with peaks in March, June, and August, corresponding to the livestock pasturing practices. We applied the Forest-based Classification and Regression algorithm to explore the relationships between the cumulative leptospirosis incidence per unit area by municipal districts (G-rate) and a number of socio-economic, landscape, and climatic factors. The model demonstrated satisfactory performance in explaining the observed disease distribution (R ² = 0.82, p < 0.01), with human population density, livestock units density, the proportion of crop area, and budgetary investments into agriculture per unit area being the most influential socio-economic variables. Climatic factors demonstrated a significantly weaker influence, with nearly similar contributions of mean yearly precipitation and air temperature and number of days with above-zero temperatures. Using a projected climate by 2100 according to the RCP8.5 scenario, we predict a climate-related rise of expected disease incidence across most of the study area, with an up to 4.4-fold increase in the G-rate. These results demonstrated the predominant influence of the population and agricultural production factors on the observed increase in leptospirosis cases in livestock animals in the Russian Arctic. These findings may contribute to improvement in the regional system of anti-leptospirosis measures and may be used for further studies of livestock leptospirosis epidemiology at a finer scale.

Keywords: ArcGIS; Arctic; G-rate; Russia; climate change; forest-based classification and regression algorithm; leptospirosis; livestock.