Mapping the risks of the spread of peste des petits ruminants in the Republic of Kazakhstan

A Scoping Review on Progression Towards Freedom from Peste des Petits Ruminants (PPR) and the Role of the PPR Monitoring and Assessment Tool (PMAT)

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants that severely threatens rural livelihoods and global food security. Under the Global Framework for the Progressive Control of Transboundary Animal Diseases (GF-TADs), the international animal health community has set the ambitious goal of eradicating PPR by 2030. However, significant disparities persist in the progression of PPR control across regions. This scoping review assesses the setbacks, deviations, and progress of 42 countries in Eastern, Western, and Northern Africa, as well as West Eurasia, toward achieving official freedom-from-PPR status. Progress was evaluated across key areas using the stepwise PPR Global Control and Eradication Strategy (GCES) approach and the PPR Monitoring and Assessment Tool (PMAT). The eligibility criteria included PubMed peer-reviewed studies, FAO/WOAH reports, presentations, guidelines, and country/region-specific PPR control plans from 2014 through 2024. The data are generated using qualitative and quantitative analyses, including spatial mapping and GCES stepwise progress evaluation. The findings reveal that many (31%) countries in the assessed regions remain in Stage 1 of the Progressive Stepwise Approach, whereas 59.5% have reached Stages 2 and 3, and only 4.8% are in Stage 4. Countries in Western Eurasia have achieved significant progress towards PPR control, with countries achieving PPR-free status, whereas, compared to Eastern and Northern Africa, the Western African region remains in the early control stages due to infrastructure gaps and resource constraints. Additionally, the recent suspension of PPR-free status in Romania, Greece and Hungary following disease emergence underscored vulnerabilities in historically free countries. The analysis results reiterate the critical role of regional collaboration, surveillance tools, and the integration of wildlife monitoring in advancing PPR control. These insights provide actionable pathways to addressing persistent barriers, highlighting the importance of adaptable, evidence-based approaches in achieving the global goal of PPR eradication by 2030.

Influence of Host and Landscape-Associated Factors in the Infection and Transmission of Pathogens: The Case of Directly Transmitted Virus in Mammals

BACKGROUND

Among pathogens associated with mammals, numerous viruses with a direct transmission route impact human, domestic and wild species health. Host and landscape factors affect viral infection and transmission dynamics of these viruses, along with barriers to host dispersal and gene exchange. However, studies show biases toward certain locations, hosts and detected pathogens, with regional variations in similar host-virus associations.

METHODS

Using a systematic review, in two electronic repositories for articles published until December 2022, we analysed the available information on host- and landscape-associated factors influencing the infection and transmission of directly transmitted viruses in mammals.

RESULTS

In the analysis, about 50% of papers examined either host traits, landscape composition or configuration measures, while approximately 24% combined host and landscape-associated factors. Additionally, approximately 17% of the articles included climatic data and 30% integrated factors related to anthropogenic impact, as these variables have a role in host density, distribution and virus persistence. The most significant and frequent host traits used as predictor variables were sex, age, body weight, host density and species identity. Land cover was the most evaluated landscape attribute, while some explored configuration variables like edge density and fragmentation indexes. Finally, temperature, precipitation and features such as human population density and human footprint index were also typically measured and found impactful.

CONCLUSION

Given the many contributions host- and landscape-related factors have in pathogen dynamics, this systematic study contributes to a better knowledge of host-virus dynamics and the identification of variables and gaps that can be used for disease prevention.

Space-time cluster analysis and maximum entropy modeling of Peste des petits ruminants (PPR) in Türkiye

Peste des petits ruminants (PPR) is an economically important highly serious transboundary disease that mainly occurs in small ruminants such as sheep and goats. The aim of this study was to identify the probability of risk and and space-time clusters of Peste des Petits Ruminants (PPR) in Türkiye. The occurrence of PPR in Türkiye from 2017 to 2019 was investigated in this study using spatial analysis based on geographic information system (GIS). Between these dates, it was determined that 337 outbreaks and 18,467 cases. The highest number of outbreaks were detected in the Central Anatolia region. It was determined that PPR is seen more intensely in sheep compared to goats in Türkiye. In this study, 34 environmental variables (19 bioclimatic, 12 precipitation, altitude and small livestock density variables) were used to explore the environmental influences on PPR outbreak by maximum entropy modeling (Maxent). The clusters of PPR in Türkiye were identified using the retrospective space-time scan data that were computed using the space-time permutation model. A PPR prediction model was created using data on PPR outbreaks combination with environmental variables. Nineteen significant (p < 0.001) space-time clusters were determined. It was discovered that the variables altitude, sheep density, precipitation in june, and average temperature in the warmest season made important contributions to the model and the PPR outbreak may be strongly related with these variables. In this study, PPR in Türkiye has been characterized significantly spatio-temporal and enviromental factors. In this context, the disease pattern and obtained these findings will contribute to policymakers in the prevention and control of the disease.

Application of modern spatio-temporal analysis technologies to identify and visualize patterns of rabies emergence among different animal species in Kazakhstan

During the period 2013-2023, 917 cases of rabies among animals were registered in the Republic of Kazakhstan. Out of these, the number of cases in farm animals amounted to 515, in wild animals to 50 and in pets to 352. Data on rabies cases were obtained from the Committee for Veterinary Control and Supervision of Kazakhstan, as well as during expeditionary trips. This research was carried out to demonstrate the use of modern information and communication technologies, geospatial analysis technologies in particular, to identify and visualize spatio-temporal patterns of rabies emergence among different animal species in Kazakhstan. We also aimed to predict an expected number of cases next year based on time series analysis. Applying the 'space-time cube' technique to a time series representingcases from the three categories of animals at the district-level demonstrated a decreasing trend of incidence in most of the country over the study period. We estimated the expected number of rabies cases for 2024 using a random forest model based on the space-time cube in Arc-GIS. This type of model imposes only a few assumptions on the data and is useful when dealing with time series including complicated trends. The forecast showed that in most districts of Kazakhstan, a total of no more than one case of rabies should beexpected, with the exception of certain areas in the North and the East of the country, where the number of cases could reach three. The results of this research may be useful to the veterinary service in mapping the current epidemiological situation and in planning targeted vaccination campaigns among different categories of animals.

Predicting Risk Areas of Classical Scrapie in China Based on Environmental Suitability

Classical scrapie is a transmissible spongiform encephalopathy that attacks the central nervous system of sheep and goats. Since its discovery in the 18th century, the disease has caused enormous economic losses and public health impacts in continental Europe. In the late 20th century, classical scrapie began to spread to places, such as Asia and the Americas, becoming a disease of global concern. In this study, based on prion occurrence records and high-resolution environmental layers, a risk assessment of classical scrapie in China was performed using a maximum entropy model. The model achieved an area under the curve value of 0.906 (95% confidence interval, 0.0883-0.0929). Sheep distribution density, road density, goat distribution density, minimum temperature of the coldest month, port density, and precipitation of the driest quarter were identified as important factors affecting the occurrence of classical scrapie. The risk map showed that potential high-risk areas in China were mainly located in Northeast China, North China, and Northwest China. This study can provide a valuable reference for the prevention of classical scrapie in China. According to the environmental variables and risk areas of classical scrapie, implementing monitoring and early warning measures in these areas is recommended to reduce the possibility of classical scrapie occurrence and transmission.

Simulating the Spread of Peste des Petits Ruminants in Kazakhstan Using the North American Animal Disease Spread Model

In this study, we simulated the potential spread of Peste des Petits Ruminants (PPR) between small ruminant (SR) farms in the Republic of Kazakhstan (RK) in case of the disease's introduction into the country. The simulation was based on actual data on the location and population of SR farms in the RK using the North American Animal Disease Spread Model (NAADSM). The NAADSM employs the stochastic simulations of the between-farm disease spread predicated on the SIR compartmental epidemic model. The most important epidemiological indicators of PPR, demography of SR farms, and livestock management characteristics in the RK were used for model parameterization. This article considers several scenarios for the initial introduction of PPR into the territory of Kazakhstan, based on previously identified high-risk regions and varying sizes of initially infected farms. It is demonstrated that the duration and size of the outbreak do not depend on the size of initially infected farms but rather depend on the livestock concentration and number of farms in the affected area. This implies that the outbreak may affect the largest number of farms in the case of introduction of the disease into farms in southern Kazakhstan. However, even in the most unfavorable scenario, the total number of affected farms does not exceed 2.4% of all SR farms in the RK. The size of the affected area is, in most cases, no larger than an averaged 2-level administrative division's size, which suggests the scale of a local epidemic. The chosen model provides ample opportunity to study the impact of different control and prevention measures on the spread of PPR as well as to assess the potential economic damage.

Spatio-temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda

Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5-2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past five years, PPR has extended from north-eastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007-2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90 - 99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new, and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and south-western Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination. This article is protected by copyright. All rights reserved.