Risk for African Swine Fever Introduction Into Kazakhstan

Schettino, Daniella N.; Abdrakhmanov, Sarsenbay; Beisembayev, Kanatzhan K.; Korennoy, Fedor I.; Sultanov, Akhmetzhan A.; Mukhanbetkaliyev, Yersyn Y.; Kadyrov, Ablaikhan; Pérez, Andrés

doi:10.3389/fvets.2021.605910

Cited by 10 publications

(14 citation statements)

References 25 publications

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“…The introduction of ASFV via the shipment of pigs was found to be the most significant risk factor for ASF transmission in other studies ( 33 , 34 ). Sharing a border with a previously-infected region increases the risk of infection during the local movement of people and domestic pigs and the migration of wild boar ( 35 ), though it was found to be statistically insignificant in our study. The summary pig population and density of smallholder farms were also identified as significant factors associated with the ASF presence in a district providing an indication of a local pig farming system density that promotes between-holdings contacts and facilitates the ASF transmission ( 36 , 37 ).…”

Section: Discussioncontrasting

confidence: 69%

Risk Factors of African Swine Fever in Domestic Pigs of the Samara Region, Russian Federation

et al. 2021

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African swine fever (ASF) is an incurable viral disease of domestic and wild pigs. A large-scale spread of ASF began in Eurasia in 2007 and has affected territories from Belgium to the Far East, occurring as both local- and regional-level epidemics. In 2020, a massive ASF epidemic emerged in the southeastern region of European Russia in the Samara Oblast and included 41 outbreaks of ASF in domestic pigs and 40 cases in wild boar. The Samara Oblast is characterized by a relatively low density of wild boar (0.04–0.05 head/km2) and domestic pigs (1.1–1.3 head/km2), with a high prevalence of small-scale productions (household farms). This study aims to understand the driving forces of the disease and perform a risk assessment for this region using complex epidemiological analyses. The socioeconomic and environmental factors of the ASF outbreak were explored using Generalized Linear Logistic Regression, where ASF infection status of the Samara Oblast districts was treated as a response variable. Presence of the virus in a district was found to be most significantly (p < 0.05) associated with the importation of live pigs from ASF-affected regions of Russia (OR = 371.52; 95% CI: 1.58–87290.57), less significantly (p < 0.1) associated with the density of smallholder farms (OR = 2.94; 0.82–10.59), volume of pork products' importation from ASF-affected regions of Russia (OR = 1.01; 1.00–1.02), summary pig population (OR = 1.01; 0.99–1.02), and insignificantly (p > 0.1) associated with presence of a common border with an ASF-affected region (OR = 89.2; 0.07–11208.64). No associations were found with the densities of pig and wild boar populations. The colocation analysis revealed no significant concentration of outbreaks in domestic pigs near cases in wild boar or vice versa. These results suggest that outbreaks notified in low biosecurity household farms were mainly associated with the transportation and trade of pigs and pork products from ASF-affected regions of Russia. The findings underline the importance of taking into account animal transportation data while conducting future studies to develop a risk map for the region and the rest of European Russia.

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Section: Discussioncontrasting

confidence: 69%

Risk Factors of African Swine Fever in Domestic Pigs of the Samara Region, Russian Federation

et al. 2021

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“…(f) Shared border with a non-CSF-free state or country was estimated using ArcGIS and dichotomized (yes, no). The relative contribution of each variable to the final risk was assumed to be similar to the weight estimated by a panel of experts for the risk of introduction of African swine fever (ASF) into a free region from a neighboring infected country described in detail elsewhere ( 41 ). Briefly, the model approach was based on a factorial design to identify 10 representative scenarios of the combination of parameters hypothesized to influence the risk of introduction of ASF (domestic pig density, wild boar density, backyard farming share, share border to a country that is infected with ASF, road density, and human density).…”

Section: Methodsmentioning

confidence: 99%

“…The MaxEnt software ( 42 ) was used for computing the maximum entropy model of wild boar distribution. The correlation between environmental layers was conducted in RStudio Team (2019) version 3.5.3 ( 35 ) using “raster” and “rgdal” packages; the packages “MASS,” “tidyverse,” and “ggbeeswarm” were used in performing the ordinal logistic regression to generate the proxy-risk for introduction of CSFv in MT considering the model developed by ASF risk prediction for Kazakhstan ( 41 ). ArcGIS 10.5.1 ( 27 ) was used for spatial data processing and mapping data and results.…”

Section: Methodsmentioning

confidence: 99%

“…(c) Human density was calculated as the population estimated in the last national census conducted in 2010 (38) for each MT municipality, divided by the area (km 2 ) of each correspondent municipality of MT, and dichotomized using 5 habitants/km 2 as the threshold (high, low); the 5 habitants/km threshold was set up because it was the approximate midpoint between the median (2.29 habitants/km 2 ) and mean (6.76 habitants/km 2 ) densities and that resulted on an acceptable 1:3 ratio for the classification of districts as high or low density-alternatively, the use of the mean and median as cutoff values for the classification did not affect the results of the regression model (data non shown). Human density was included as a proxy for the movement of people (tourists or workers) that can carry contaminated food that can be disposed and accessed by wild boars (20,(39)(40)(41). (d) Road density was calculated using ArcGIS, considering the layers of municipalities and layers of roads of MT and dichotomized using the median (50% high, 50% low); road density was included because the introduction and spread of the disease may be influenced by human activities that could increase the risk for contacts with wild boars (37).…”

Section: Analytical Frameworkmentioning

confidence: 99%

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Risk of Introduction of Classical Swine Fever Into the State of Mato Grosso, Brazil

2021

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Classical swine fever (CSF) is considered one of the most important diseases of swine because of the far-reaching economic impact the disease causes to affected countries and regions. The state of Mato Grosso (MT) is part of Brazil's CSF-free zone. CSF status is uncertain in some of MT's neighboring States and countries, which has resulted in the perception that MT is at high risk for the disease. However, the risk for CSF introduction into MT has not been previously assessed. Here, we estimated that the risk for CSF introduction into the MT is highly heterogeneous. The risk associated with shipment of commercial pigs was concentrated in specific municipalities with intense commercial pig production, whereas the risk associated with movement of wild boars was clustered in certain municipalities located close to the state's borders, mostly in northern and southwestern MT. Considering the two pathways of possible introduction assessed here, these results demonstrate the importance of using alternative strategies for surveillance that target different routes and account for different likelihoods of introduction. These results will help to design, implement, and monitor surveillance activities for sustaining the CSF-free status of MT at times when Brazil plans to expand the recognition of disease-free status for other regions in the country.

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“…Detailed data on the distribution of small ruminants in Kazakhstan were obtained during a nationwide livestock survey undertaken by the research team members from 2018 to 2019 (Schettino et al, 2021).…”

Section: Data Sourcesmentioning

confidence: 99%

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

Abdrakhmanov

Mukhanbetkaliyev

Sultanov

et al. 2021

Transbounding Emerging Dis

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Peste des petits ruminants (PPR) is a viral transboundary disease seen in small ruminants, that causes significant damage to agriculture. This disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK's territory to the spread of the disease in the event of its importation from infected countries. The negative binomial regression model that was trained on the PPR outbreaks in China, was used to rank municipal districts in the RK in terms of PPR spread risk. The outbreak count per administrative district was used as a risk indicator, while a number of socio-economic, landscape, and climatic factors were considered as explanatory variables. Summary road length, altitude, the density of small ruminants, the maximum green vegetation fraction, cattle density, and the Engel coefficient were the most significant factors. The model demonstrated a good performance in training data (R 2 = 0.69), and was transferred to the RK, suggesting a significantly lower susceptibility of this country to the spread of PPR. Hot spot analysis identified three clusters of districts at the highest risk, located in the western, eastern, and southern parts of Kazakhstan. As part of the study, a countrywide survey was conducted to collect data on the distribution of livestock populations, which resulted in the compilation of a complete geo-database of small ruminant holdings in the RK.The research results may be used to formulate a national strategy for preventing the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

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Risk for African Swine Fever Introduction Into Kazakhstan

Cited by 10 publications

References 25 publications

Risk Factors of African Swine Fever in Domestic Pigs of the Samara Region, Russian Federation

Risk Factors of African Swine Fever in Domestic Pigs of the Samara Region, Russian Federation

Risk of Introduction of Classical Swine Fever Into the State of Mato Grosso, Brazil

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

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