Yersyn Y. Mukhanbetkaliyev

Highly pathogenic avian influenza (HPAI) is a transboundary disease of birds with zoonotic potential. Since 2021, H5 HPAI virus variants have spread globally, with migratory waterfowl playing a key role in transcontinental dissemination. Kazakhstan lies at the intersection of major flyways, creating a persistent risk of virus introduction into domestic poultry. While HPAI risk in wild birds has been explored in Kazakhstan, the spatial risk for poultry has remained underassessed. Here, we present a risk map for HPAI in poultry across 174 administrative districts of Kazakhstan, using a multi-criteria decision analysis (TOPSIS) that integrates five quantitative risk indicators that relate to wild bird habitat, virus survival in the environment and poultry farm census. The obtained district-level risk index agreed well with historical outbreak data (2005–2025), with an area under the receiver operating characteristic (ROC) curve of 0.899 (95% CI: 0.846–0.952). Most districts (56%) were at negligible risk, whereas medium- and high-risk clusters occurred in the north, central, and southern regions, and near the Caspian Sea. These findings provide an operational, spatially explicit tool for targeting surveillance and biosecurity measures to high-risk areas, ultimately helping to mitigate the impact of one of the most devastating transboundary poultry diseases in Central Asia.

Background and Aim. Highly pathogenic avian influenza (HPAI) remains one of the most significant transboundary avian diseases, affecting affecting farmed, synanthropic, and wild bird populations. Migratory wild birds play a crucial role in the long-distance spread of avian influenza viruses across continents. The aim of this study is to develop an interactive geospatial dashboard for monitoring and assessing risk factors associated with HPAI along migration routes in Kazakhstan. Materials and Methods. Relevant spatial datasets related to the risk of avian influenza spread were integrated into an interactive geospatial dashboard. The analysis included bird migration and ringing data from EURING and the Institute of Zoology, georeferenced wild bird observations from the eBird platform (25,828 records representing 137 species), and outbreak data from the FAO EMPRES-i and WOAH WAHIS databases. Spatial analysis and data processing were performed using ArcGIS Pro, ArcGIS Online, and the R statistical environment, while species spatial distribution modeling was performed using the MaxEnt algorithm. Results. Through the integration and analysis of heterogeneous spatial datasets, an interactive geospatial dashboard was developed to assess the spatial distribution and potential spread of highly pathogenic avian influenza (HPAI) in Kazakhstan. The platform combines wild bird observation data from the eBird database, bird ringing and migration data, water body and wetland data, poultry density information, and officially recorded disease outbreaks. Interactive filtering tools allow data to be analyzed by geographic region, taxonomic group, and risk level. Spatial overlay analysis revealed overlap between areas of high wild bird activity and regions with intensive poultry farming, indicating potential ecological interfaces for virus transmission. The developed dashboard provides multi-layered visualization of epizootological data and supports rapid analysis of potential HPAI risk zones in Kazakhstan. Conclusion. The developed geospatial dashboard is an effective decision-support tool for veterinarysurveillance and epidemiological risk assessment. The integration of environmental and epidemiologicaldata enables the rapid identification of potential high-risk areas for the introduction and spread of highlypathogenic avian influenza, facilitating the development of evidence-based surveillance strategies.

Background and Aim. Highly pathogenic avian influenza (HPAI) remains a serious threat to industrial poultry farming and human health. The virus is highly variable and can be transmitted through the migratory flows of wild birds, which contribute to its widespread distribution. This study aimed to analyze the epidemiology of HPAI in Kazakhstan and assess the role of migratory birds therein. Materials and Methods. The study assessed data on outbreaks of HPAI in Kazakhstan for the period 2005 to 2024. Information on migratory bird species in Kazakhstan territories and their migration routes was obtained by the “Institute of Zoology”. The research applied classical methods of epidemiological analysis, statistical modeling of epizootics, and data visualization using ArcGIS Pro software. Results. Over the past 20 years (2005 to 2024), outbreaks of HPAI have been periodically recorded in Kazakhstan, with cases of infection in both domestic and wild birds. The pathogens mainly comprised viruses of the H5N1, H5N8, and other H5 strains. Wild migratory birds played the main role in the emergence and spread of HPAI. The territory of Kazakhstan contained the convergence point of two of their most important migration routes: the Central Asian-Indian and West Asian-African routes. A total of 489 bird species has been registered in Kazakhstan, most of which are seasonal migrants flying through the country; accordingly, they have a significant impact on the epizootic process of HPAI. Conclusion. Analysis of epizootiological data showed that the dates and locations of HPAI outbreaks directly correlated with the stages and routes of the seasonal migration of migratory birds. Predictive modeling of virus spread showed that the northern and western regions of the country, where most of the country’s poultry farms are located, were at the highest risk.

Background and Aim. Rabies is a zoonotic viral disease that continues to pose a major public health threat worldwide, particularly in regions with limited access to vaccination and disease monitoring. Kazakhstan remains endemic for animal rabies, with variable patterns across geography and species. The objective of this study is to identify spatial and temporal clusters of rabies outbreaks among animals in Kazakhstan from 2013 to May 2025, using GIS-based methods to inform regional control and prevention strategies. Materials and Methods. This study applied three geospatial tools within ArcGIS ProHot Spot Analysis using Getis-Ord Gi*, Kernel Density Estimation (KDE), and Space-Time Kernel Density to a national dataset of confirmed rabies cases among animals. The database included geographic coordinates, species type, dates of confirmation, and number of infected animals. KDE and Getis-Ord Gi* were used to detect spatial clusters and density gradients, while Space-Time Kernel Density enabled tracking of outbreak patterns over time. Results. Hot Spot Analysis identified high-risk zones in northeastern (North Kazakhstan, Pavlodar oblasts) and western regions (Aktobe oblast), potentially linked to wild animal migration and insufficient vaccination coverage. KDE revealed additional high-density areas in southern and eastern regions. Space-time analysis showed persistent clusters in Zhambyl, Kostanay oblasts, and western Kazakhstan, while central regions exhibited low density likely due to geographic and demographic isolation. Conclusion. Spatial and spatiotemporal analyses demonstrated that rabies outbreaks in Kazakhstan are not randomly distributed but form distinct species- and region specific clusters. These insights support the need for differentiated veterinary approaches. Timely and geographically targeted vaccination programs particularly in identified hot spots are critical to reducing the incidence of rabies.

Background and Aim. Despite significant success in the fight against anthrax, cases of animal and human disease are still registered in Kazakhstan. The presence of many permanently unfavorable points with numerous anthrax burial sites contributes to the maintenance of epidemiological unfavorability in the country. Moreover, active human activity in potentially dangerous zones often contributes to the activation of the epidemic process and the emergence of new infection outbreaks. The purpose of this research was to study the degree of influence of various anthropogenic factors on the epidemiology of anthrax in Kazakhstan. Materials and Methods. The analysis object was information about anthrax outbreaks in Ka-zakhstan from 1933-2024. The study materials were data from the cadastre of stationary points unfavorable for anthrax, materials for statistical veterinary reporting, and our own records obtained during expedition trips. The data were analyzed using system modeling (Monte Carlo method) and spatial geographical analysis via the Moran autocorrelation method. Results. In total, 4,089 outbreaks of anthrax in Kazakhstan occurred during the study period. In chronological terms, 5 historical periods were distinguished, and the analysis of the available epidemiological data for 1933–2024 indicates that the main periods of increase and decrease in the annually registered anthrax outbreaks correspond to certain periods of economic and socioeconomic change in the country and the introduction of antiepizootic measures against this infection. Conclusion. The analysis shows a pronounced uneven temporal and spatial distribution of anthrax foci and significant differences in the dynamics of the epizootic process in Kazakhstan. At the moment, new information is being formed on the factors influencing the ecology and epidemiology of the disease, which are associated with various forms of organization and management of the economy, urbanization of the population, changes in their social conditions and the influence of other anthropogenic factors.

Highly pathogenic avian influenza (HPAI) is a highly contagious disease of domestic, synanthropic, and wild birds that has demonstrated a sharp rise globally since 2020. This study intends to examine environmental and demographic factors most significantly associated with HPAI (H5N1 and H5N8) outbreaks in Kazakhstan, 2020–2024, and to identify areas of potential underreporting of the disease. Two ecological niche models were developed, namely an “occurrence model” (considering climatic and environmental factors influencing the likelihood of HPAI occurrence) and a “reporting model” (that assesses the probability of disease reporting based on human and poultry population demography). Both models were trained using outbreak locations in countries neighboring Kazakhstan (Afghanistan, China, Hong Kong, Iran, Iraq, Pakistan and Russia), and then tested using the HPAI outbreak locations in Kazakhstan. Results suggested a good fit for both models to Kazakhstani outbreaks (test AUC = 0.894 vs. training AUC = 0.915 for “occurrence model”, and test AUC = 0.869 vs. training AUC = 0.872 for “reporting model”). A cluster of high occurrence-to-reporting ratio was detected in the south-western region of Kazakhstan, close to the Caspian Sea, suggesting a need for enhancing surveillance efforts in this zone as well as in some other areas of Pavlodar, Northern Kazakhstan, Western Kazakhstan, Qyzylorda, and Eastern Kazakhstan. Results presented here will help inform the design and implementation of control strategies for HPAI in Kazakhstan with the ultimate goal of promoting disease prevention and control in the country.

Highly pathogenic avian influenza (HPAI) is a highly contagious disease of domestic, synanthropic, and wild birds that has demonstrated a sharp rise globally since 2020. The study here is intended to assess the quality of reporting HPAI outbreaks in Kazakhstan using outbreaks’ location in neighboring countries (2020 – 2024) as training data to build an ecological niche model of suitability to the disease in the country. Afghanistan, China, Hong Kong, Iran, Iraq, Pakistan and Russia were chosen as training countries for fitting a Maximum entropy (Maxent) model, which was then tested using the locations of HPAI outbreaks in Kazakhstan. The results suggest good performance of the model in explaining Kazakhstani outbreaks (test AUC = 0.88 vs AUC=0.93 for training data). HPAI outbreaks reported in Kazakhstan fall mainly in areas treated as highly suitable by the model. The number of outbreaks per each analyzed country is also well explained by a summary area of suitable places per country as predictor (p2=0.75), with Kazakhstan demonstrating no significant deviation from the found dependency. Results suggest that, although sensitivity of the surveillance system in the country is uncertain, it may be able to accomplish the objective of identifying regions affected by the virus incursions.

Equine herpesvirus 1 (EHV‐1) is one of the most dangerous viral diseases affecting ungulates, and is characterized by a wide range of clinical manifestations in horses, including rhinopneumonia, abortion, neonatal death, and myeloencephalopathy. It is well known for causing mass abortions in mares and respiratory diseases in young animals. Once introduced into a horse breeding farm of any type, EHV‐1 tends to establish as a persistent infection. The disease is reported on nearly all continents and causes substantial annual economic losses to horse breeding operations. In Kazakhstan, 34 EHV‐1 outbreaks were recorded between 2017 and 2024. The objective of our study was to identify potential risk factors associated with the presence of EHV‐1 within the study area. We employed a forest‐based classification and regression approach to explore a set of sociodemographic, environmental, and transportation‐related factors associated with the presence or absence of EHV‐1 at the level of administrative regions. A standard set of explanatory variables was supplemented with horse population density, derived from demographic data of horse‐breeding farms obtained through a nationwide survey. Modeling results indicated that the most significant factor influencing EHV‐1 presence was the average wind speed in January, followed by road density, the number of horse farms, and the number of livestock‐related facilities targeted for surveillance. Horse population density was found to be among the least significant variable in the model. The resulting risk map highlights areas with a higher suitability for EHV‐1 emergence, primarily located in regions with moderate‐to‐high horse population densities and characterized by steppe‐ and grassland‐type landscapes, which are predominantly found in the northern, central, and south‐western parts of Kazakhstan. These findings can serve as a foundation for further investigation into the spatial patterns of EHV‐1 in the country and for enhancing veterinary surveillance and control measures.

Background and Aim: Theileria annulata infection in cattle causes major economic losses in livestock production in many Central Asian countries, including the southern region of Kazakhstan. This study aimed to obtain a recombinant T. annulata surface protein (TaSP) and to investigate its possible use as an antigen in an indirect enzyme-linked immunosorbent assay (ELISA) for the serological diagnosis of bovine theileriosis. Materials and Methods: Recombinant TaSP was obtained by cloning a polymorphic region of the TaSP gene, expressing it in Escherichia coli strain BL21, and purifying it by metal chelating chromatography. An indirect ELISA using recombinant TaSP as an antigen was developed and evaluated for the detection of T. annulata-specific antibodies in plasma samples from 69 cows polymerase chain reaction (PCR)-positive or PCR-negative for T. annulata and/or Theileria orientalis from southern Kazakhstan. Results: The obtained recombinant protein had a molecular weight of 32 kDa, and mass spectrometry analysis of the purified protein identified it as a fragment of the surface protein of T. annulata. Initial testing of 69 field plasma samples from cattle showed that the results of indirect ELISA using TaSP as an antigen agreed substantially with those of T. annulata PCR (κ: 0.78). The relative sensitivity and specificity of indirect ELISA were 88.7% and 100%, respectively, using PCR as a reference. There was no evidence of cross-reaction with T. orientalis. Conclusion: Initial results using recombinant TaSP as an antigen in indirect ELISA are promising and support the widespread use of this assay for routine diagnosis and T. annulata seroprevalence studies in cattle in Kazakhstan and possibly neighboring countries. Keywords: cattle, indirect enzyme-linked immunosorbent assay, Kazakhstan, recombinant T. annulata surface protein, Theileria annulata.

Objects for Targeted Surveillance (OTS) are infrastructure entities that can be considered as focal points and conduits for transmitting infectious animal diseases, necessitating ongoing epidemiological surveillance. These entities encompass slaughterhouses, meat processing plants, animal markets, burial sites, veterinary laboratories, etc. Currently, in Kazakhstan, a funded research project is underway to establish a Geographic Information System (GIS) database of OTSs and investigate their role in the emergence and dissemination of infectious livestock diseases. This initial investigation examined the correlation between brucellosis outbreaks in cattle and small ruminant farms in the southeastern region of Kazakhstan and the presence of OTSs categorized as “slaughterhouses,” “cattle markets,” and “meat processing plants. The study area (namely Qyzylorda, Turkestan, Zhambyl, Almaty, Zhetysu, Abay and East Kazakhstan oblasts), characterized by the highest livestock density in the country, covers 335 slaughterhouses (with varying levels of biosecurity), 45 livestock markets and 15 meat processing plants. Between 2020 and 2023, 338 cases of brucellosis were reported from livestock farms in this region. The findings of the regression model reveal a statistically significant (p<0.05) positive association between the incidence of brucellosis cases and the number of OTSs in the region. Conversely, meat processing plants and livestock markets did not exhibit a significant influence on the prevalence of brucellosis cases. These results corroborate the hypothesis of an elevated risk of brucellosis transmission in regions with slaughterhouses, likely attributable to increased animal movements within and across regions, interactions with vehicles and contact with slaughterhouse staff. These outcomes mark a pivotal advancement in the national agricultural development agenda. The research will be extended to encompass the entire country, compiling a comprehensive OTS database.