The Yamal Peninsula in the Russian Federation experienced a massive outbreak of anthrax in reindeer (Rangifer tarandus) in July–August 2016, with 2,650 (6.46% of the total susceptible population) animals infected, of which 2,350 died (case fatality rate of 88.67%). In our study, we analyzed climatic and epidemiological factors that could have triggered the outbreak. The cancelation of reindeer vaccination against anthrax in 2007 resulted in an increase in population susceptibility. In response to the outbreak, total vaccination of all susceptible animals was resumed. To assess the vaccination effectiveness, we tested 913 samples of blood serum taken from vaccinated reindeer using an antigenic erythrocyte diagnostic kit to detect specific anti-anthrax antibodies via an indirect hemagglutination assay (IHA) 9 months after vaccination. We found that 814 samples had sufficiently high levels of anti-anthrax antibodies to indicate a protection level of 89% (95% confidence interval: 87–91%) of the whole reindeer population. Abnormally high ambient temperature in the summer of 2016 contributed to the thawing of permafrost and viable Bacillus anthracis spores could have become exposed to the surface; the monthly average air temperatures in June, July, and August 2016 were 20–100% higher than those of the previous 30-year period, while the maximum air temperatures were 16–75% higher. Using the projected climate data for 2081–2100 according to the “worst case” RCP8.5 scenario, we demonstrated that the yearly air temperature may average above 0°C across the entire Yamal Peninsula, while the yearly number of days with a mean temperature above 0°C may rise by 49 ± 6 days, which would provide conditions for reactivation of soil anthrax reservoirs. Our results showed that the outbreak of anthrax occurred under conditions of a significant increase in air temperature in the study area, underlined the importance of vaccination for controlling the epidemic process, and demonstrated the effectiveness of monitoring studies using the IHA diagnostic kit for detecting erythrocyte anthrax antigens.

Listeriosis is one of the most significant humans and animals foodborne infectious diseases. Here, we characterized 48 Listeria monocytogenes strains isolated in the territory of inner Eurasia during the second half of the 20th century. A total of 23 strains (52.3%) were susceptible to the nine antibiotics tested, 30.43%, 15.22%, and 8.7% were resistant penicillin G, ampicillin, and enrofloxacin, respectively. We applied the multilocus sequence typing (MLST) scheme to determine the phylogenetic positions of the strains. All but one strain belonged to the II phylogenetic lineage, and the majority of the strains belonged to one of the previously described clonal complexes (CCs). More than 60% of the strains belonged to the clonal complex CC7 that prevailed among all sources, including cattle (58%), small ruminants (64%), rodents (71%), and humans (50%). Further, CC7, CC101, and CC124 were found among human isolates. The MLST scheme was supplemented with virulence gene analysis. In total, eight inlA, six inlB, and six inlC allelic variants were found, and all but one strain carried one of the two inlE alleles. Most strains (62.5%) belonged to the same multivirulence locus sequence typing (MvLST) type, which includes CC7, inlA allele 4, inlB allele 14, inlC allele 6, and inlE allele 8.

Leptospirosis is a zoonotic natural focal disease caused by the pathogenic bacteria Leptospira. Its spread is related to certain ecological factors. The aim of the current research was to assess potential exposure to the infection as a function of environmental determinants in the Republic of Sakha (Yakutia), Russian Federation. We applied environmental niche modeling using leptospirosis cases in livestock and wild animals in 1995–2019 with regard to a set of landscape, climatic, and socioeconomic variables, both for the current climate and for the projected climate for 2041–2060. The MaxEnt model performed well (AUC = 0.930), with the mean temperature of the warmest quarter, mean diurnal range, land cover type, and altitude being the most contributing variables. Consequent zoning based on the proportion of high-risk cells within each administrative unit suggested that five out of the 36 districts of the Republic are at high risk in the current climate conditions, with three more districts expected to demonstrate a high risk by 2060. This study presents the first-ever attempt at leptospirosis ecological modeling in Russia. Its results correspond well to the findings of other authors and underline the importance of considering ecological factors when conducting a leptospirosis risk assessment.

African swine fever (ASF) is a transboundary viral disease that affects both domestic pigs and wild boar, causing enormous damage to the pig production. Numerous studies are devoted to elucidating the role of domestic and wild pigs in ASF outbreaks. The identification of the risk factors contributing to the spread of disease in previously unaffected regions is of special interest. We conducted a retrospective analysis of sporadic ASF outbreaks in the Nizhny Novgorod region of the Russian Federation from 2011 to 2022 among both domestic pig and wild boar populations. Methods of spatiotemporal cluster analysis, mathematical modeling with logistic and negative binomial regressions, as well as the cross-correlation analysis of time series, were used to identify the most significant factors associated with the ASF outbreaks’ presence and intensity. Regression analysis did not reveal a statistically significant correlation between ASF outbreaks in wild boar and their population density over the entire study period. However, the spatial and temporal coincidence between outbreaks and increased wild boar density was observed at the beginning of the ASF epidemic in the region. We identified the significant environmental and sociodemographic factors contributing to the spread of ASF in both domestic pig and wild boar populations. The number of small-scale farms (backyards) and summary road length were determined as the main factors contributing to the spread of ASF among domestic pigs in the Nizhny Novgorod region. The regression model for ASF outbreaks in the wild boar population revealed the presence of water bodies, number of hunting farms, and the occurrence of ASF outbreaks in the domestic pig population to be the main explanatory factors. A statistically significant coincidence was identified between the monthly volume of pork products imported to the region from the rest of Russia and the occurrence of ASF outbreaks in both domestic and wild populations. Thus, the identified spatiotemporal patterns may be useful to determine areas with an elevated risk of ASF outbreaks emergence for better resource management, disease control, and prevention. The importance of intensified monitoring and control measures in the pig products supply chain is strongly needed. The obtained models can be applied not only by the veterinary service of the Nizhny Novgorod region but to other regions with similar environmental and sociodemographic conditions.

African swine fever (ASF) is an infectious disease that affects both domestic pigs (DPs) and wild boar (WB). The WB population plays an important role in the spread of ASF as the WB acts as a natural reservoir of the virus and transmits it to other susceptible wild and domestic pigs. Our study was aimed at revealing the areas with a high concentration of the WB population, and their potential relationships with the grouping of ASF cases in WB during the course of the ASF spread in the Russian Federation (2007–2022). We collected the annual data on WB numbers by municipalities within the regions of the most intensive ASF spread. We then conducted spatiotemporal analysis to identify clustering areas of ASF cases and compare them with the territories with a high density of WB population. We found that some of the territories with elevated ASF incidence in WB demonstrated spatial and temporal coincidence with the areas with a high WB population density. We also visualized the zones (“emerging hot spots”) with a statistically significant rise in the WB population density in recent years, which may be treated as areas of paramount importance for the application of surveillance measures and WB population control.