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

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 and 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 5 years, PPR has extended from northeastern 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 southwestern 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

Whole-genome sequencing of African swine fever virus from wild boars in the Kaliningrad region reveals unique and distinguishing genomic mutations

IntroductionSince the first report of outbreaks of African swine fever (ASF) in Georgia in 2007, the disease has expanded into Europe, Russia, and Asia, spreading rapidly via contact with infected animals including domestic pigs and wild boars. The vast expansion of this Genotype II African swine fever virus (ASFV) across wide-ranging territories and hosts inevitably led to the acquisition of novel mutations. These mutations could be used to track the molecular epidemiology of ASFV, provided that they are unique to strains restricted within a certain area. Whilst whole-genome sequencing remains the gold standard for examining evolutionary changes, sequencing of a single locus with significant variation and resolution power could be used as a rapid and cost-effective alternative to characterize multiple isolates from a single or related outbreak.Material and methodsASFVs obtained during active ASF outbreaks in the Russian region of Kaliningrad between 2017 and 2019 were examined. Since all of the viruses belonged to Genotype II and no clear differentiation based on central variable region (CVR) sequencing was observed, the whole-genome sequences of nine ASFV isolates from this region were determined. To obtain insights into the molecular evolution of these isolates, their sequences were compared to isolates from Europe, Asia, and Africa.ResultsPhylogenetic analysis based on the whole-genome sequences clustered the new isolates as a sister lineage to isolates from Poland and Germany. This suggests a possible shared origin followed by the addition of novel mutations restricted to isolates from this region. This status as a sister lineage was mirrored when analyzing polymorphisms in MGF-505-5R and MGF-110-7L, whilst a polymorphism unique to sequences from Kaliningrad was identified at locus K145R. This newly identified mutation was able to distinguish the isolates obtained from Kaliningrad with sequences of Genotype II ASFVs available on GenBank.DiscussionThe findings of this study suggest that ASFVs circulating in Kaliningrad have recently obtained this mutation providing an additional marker to the mutations previously described

Rabies in the Republic of Kazakhstan: spatial and temporal characteristics of disease spread over one decade (2013–2022)

Rabies is a fatal zoonotic disease that remains endemic in Kazakhstan despite the implementation of annual vaccination campaigns. Using data collected over a 10-year time period, the objective of this study was to provide updated information on the epidemiological situation of the disease in the country, and quantitative data on the species-specific spatial distribution of rabies and on the epidemiological features associated with that clustering. Five significant (p < 0.05) clusters of disease were detected. Clusters in southern Kazakhstan were associated with companion animals, which are likely explained by the maintenance of a domestic cycle of the disease in the most densely populated region of the country. Livestock cases were most frequent in clusters in the eastern (where wildlife cases were also frequent) and western regions of Kazakhstan, with higher probability of occurrence in spring and summer, compared to the rest of the year. The results here are consistent with differential patterns for disease transmission in Kazakhstan and will contribute to the design and implementation of zoning approaches to support the progressive control of rabies in the country

Comparison of Spatiotemporal Patterns of Historic Natural Anthrax Outbreaks in Minnesota and Kazakhstan (Supplementary data)

1. Minnesota data are summarized by year (1912-2014) by county (n=87) 2. Kazakhstan data are summarized by year (1933-2014) by district (n=163)We compared the spatiotemporal patterns of historic animal Anthrax records in Minnesota and Kazakhstan. In Minnesota, 289 animal Anthrax cases reported between 1912 and 2014 to the Minnesota Board of Animal Health were used in the analysis. For events occurred between 1920 and 1999 the geographical coordinates were obtained using historic aerial images whereas, for those cases that occurred after 2000, coordinates were recorded during site visits. For the Republic of Kazakhstan, laboratory confirmed Anthrax cases reported by the Cadastral register of stationary unfavorable foci on Anthrax between 1933 and 2014 (n=3,997) were analyzed. Because of the sensitivity of providing the actual geographical locations/coordinates, these data on reported Anthrax cases were summarized by administrative unit, by year. The administrative units were Minnesota counties and districts of Kazakhstan. This repository contains two separate EXCEL sheets summarizing the data accordingly.This study was funded in part by: 1) the Minnesota Discovery, Research, and Innovation Economy (MnDRIVE) program of the Office of the Vice President for Research (OVPR) of the University of Minnesota and 2) Scientific Thematic “Zonification of Kazakhstan according to biosecurity categories with regard to dangerous infectious animal diseases” under the Program #249 of funding scientific researches in agro-industry and environmental management by the Ministry of Agriculture of Kazakhstan

Genetic Characterization of the Central Variable Region in African Swine Fever Virus Isolates in the Russian Federation from 2013 to 2017

African swine fever virus (ASFV), classified as genotype II, was introduced into Georgia in 2007, and from there, it spread quickly and extensively across the Caucasus to Russia, Europe and Asia. The molecular epidemiology and evolution of these isolates are predominantly investigated by means of phylogenetic analysis based on complete genome sequences. Since this is a costly and time-consuming endeavor, short genomic regions containing informative polymorphisms are pursued and utilized instead. In this study, sequences of the central variable region (CVR) located within the B602L gene were determined for 55 ASFV isolates submitted from 526 active African swine fever (ASF) outbreaks occurring in 23 different regions across the Russian Federation (RF) between 2013 and 2017. The new sequences were compared to previously published data available from Genbank, representing isolates from Europe and Asia. The sequences clustered into six distinct groups. Isolates from Estonia clustered into groups 3 and 4, whilst sequences from the RF were divided into the remaining four groups. Two of these groups (5 and 6) exclusively contained isolates from the RF, while group 2 included isolates from Russia as well as Chechnya, Georgia, Armenia, Azerbaijan and Ukraine. In contrast, group 1 was the largest, containing sequences from the RF, Europe and Asia, and was represented by the sequence from the first isolate in Georgia in 2007. Based on these results, it is recommended that the CVR sequences contain significant informative polymorphisms to be used as a marker for investigating the epidemiology and spread of genotype II ASFVs circulating in the RF, Europe and Asia

Zoning the territory of the Republic of Kazakhstan as to the risk of rabies among various categories of animals

This paper presents the zoning of the territory of the Republic of Kazakhstan with respect to the risk of rabies outbreaks in domestic and wild animals considering environmental and climatic conditions. The national database of rabies outbreaks in Kazakhstan in the period 2003-2014 has been accessed in order to find which zones are consistently most exposed to the risk of rabies in animals. The database contains information on the cases in demes of farm livestock, domestic animals and wild animals. To identify the areas with the highest risk of outbreaks, we applied the maximum entropy modelling method. Designated outbreaks were used as input presence data, while the bioclim set of ecological and climatic variables, together with some geographic factors, were used as explanatory variables. The model demonstrated a high predictive ability. The area under the curve for farm livestock was 0.782, for domestic animals -0.859 and for wild animals - 0.809. Based on the model, the map of integral risk was designed by following four categories: negligible risk (disease-free or favourable zone), low risk (surveillance zone), medium risk (vaccination zone), and high risk (unfavourable zone). The map was produced to allow developing a set of preventive measures and is expected to contribute to a better distribution of supervisory efforts from the veterinary service of the country

Spatio-temporal analysis and visualisation of the anthrax epidemic situation in livestock in Kazakhstan over the period 1933-2016

An analysis of the anthrax epidemic situation among livestock animals in the Republic of Kazakhstan over the period 1933-2016 is presented. During this time, 4,064 anthrax outbreaks (mainly in cattle, small ruminants, pigs and horses) were recorded. They fall into five historical periods of increase and decrease in the annual anthrax incidence (1933-1953; 1954-1968; 1969-1983; 1984- 2001; and 2002-2016), which has been associated with changes in economic activity and veterinary surveillance. To evaluate the temporal trends of incidence variation for each of these time periods, the following methods were applied: i) spatio-temporal analysis using a space-time cube to assess the presence of hotspots (i.e., areas of outbreak clustering) and the trends of their emergence over time; and ii) a linear regression model that was used to evaluate the annual numbers of outbreaks as a function of time. The results show increasing trends during the first two periods followed by a decreasing trend up to now. The peak years of anthrax outbreaks occurred in 1965-1968 but outbreaks still continue with an average annual number of outbreaks of 1.2 (95% confidence interval: 0.6-1.8). The space-time analysis approach enabled visualisation of areas with statistically significant increasing or decreasing trends of outbreak clustering providing a practical opportunity to inform decision-makers and allowing the veterinary services to concentrate their efforts on monitoring the possible risk factors in the identified locations