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African swine fever ASF is a disease of swine that is endemic to some African countries and that has rapidly spread since through many regions of Asia and Europe, becoming endemic in some areas of those continents. Since there is neither vaccine nor treatment for ASF, prevention is an important action to avoid the economic losses that this disease can impose on a country.

Although the Republic of Kazakhstan has remained free from the disease, some of its neighbors have become ASF-infected, raising concerns about the potential introduction of the disease into the country. Here, we have identified clusters of districts in Kazakhstan at highest risk for ASF introduction.

Questionnaires were administered, and districts were visited to collect and document, for the first time, at the district level, the distribution of swine operations and population in Kazakhstan. A snowball sampling approach was used to identify ASF experts worldwide, and a conjoint analysis model was used to elicit their opinion in relation to the extent at which relevant epidemiological factors influence the risk for ASF introduction into disease-free regions.

The resulting model was validated using data from the Russian Federation and Mongolia. Finally, the validated model was used to rank and categorize Kazakhstani districts in terms of the risk for serving as the point of entry for ASF into the country, and clusters of districts at highest risk of introduction were identified using the normal model of the spatial scan statistic. Results here will help to allocate resources for surveillance and prevention activities aimed at early detecting a hypothetical ASF introduction into Kazakhstan, ultimately helping to protect the sanitary status of the country.

African swine fever ASF is a viral disease of pigs, affecting members of the Suidae family domestic pigs or wild boars without differentiation of age or sex. ASFv infection has an impact on international trade in pigs and pork products, being a threat to global food security; hence, the disease is notifiable to the World Organization for Animal Health OIE.

ASF epidemics also represent a public health issue because they disrupt the value chain and access to international markets, limiting food access to the population in affected regions and trade partners 1 — 4.

Control measures for ASF are based on biosecurity measures as neither a licensed vaccine nor any treatments are currently available 5. The ASFv was introduced in into Georgia, from where the virus spread throughout the Caucasus region Armenia and Azerbaijan and the Russian Federation, where the disease became endemic. Since , more than 7,, pigs were culled or destroyed in Asia, causing far-reaching economic losses to the region.

The unprecedented ASFv spread through Asia and Europe has resulted in great concern for many free countries and regions worldwide 1 , Kazakhstan is a land-locked country located in the transition of Eastern Europe and Central Asia, sharing extensive borders with three countries Russian Federation, Mongolia, and China that have been infected by the ASFv. Nevertheless, there is still a potential for increasing the exporting of pork products in association with bans imposed to ASF-infected countries and the consequent increase in demand in importing markets.

For those reasons and given that Kazakhstan is still free from the disease, there is an urgent need to increase preparedness for enhancing the chances of early detecting and mitigating a hypothetical ASFv introduction into the country. Because ASF has never been reported in Kazakhstan, there is no information on the socioeconomic or environmental factors associated with the disease spread in the country.

For that reason, the allocation of resources in preventive measures that are effective in minimizing the risk of disease incursion is particularly challenging in Kazakhstan. ASF may be introduced into free areas through different pathways, such as trade of live pigs and pork products, wild boar transboundary movements, and contacts with free-ranging pigs, fomites, and vehicles.

The objective of this paper was to identify the areas of Kazakhstan that are most likely to serve as port of entry for a hypothetical ASFv incursion into the country. Results will help the public veterinary authority of Kazakhstan to selectively allocate financial and human resources to target surveillance activities in districts with the highest predicted risk for disease introduction.

Additionally, the methodological approach applied here may be used for ranking regions in ASF-free countries located in affected regions worldwide, with the ultimate goal of designing and implementing surveillance programs to prevent and mitigate the impact of the disease 13 , Because data on the distribution of the susceptible swine population at the district level in Kazakhstan were not available, a country-wise survey was undertaken, aimed at the creation of a national database of pig-related operations.

The survey was conducted in — as a series of trips in close collaboration with regional authorities and veterinary services. Locations of all facilities related to the swine industry were georeferenced, and relevant attributes were recorded. The work resulted in the construction of a unique national database of pig holdings as well as slaughterhouses, meat storage, and processing facilities and retail stores.

Additionally, data on other relevant variables, as number of pigs per farm and type of pig production based on the ownership of the farms, were compiled and organized in ad hoc databases.

The database enabled the calculation of pig density and backyard farming share for Kazakhstan used in the present study.

The sources of other data used here are provided later at Table 2. Conjoint analysis, which is a marketing research tool used in surveys aimed at capturing the best fit decision of costumers and determining tradeoffs 16 , was used in the current study.

Districts in a hypothetical ASF-free country located in an ASF-infected region were designed using a factorial design to balance the distribution of epidemiological features hypothesized to influence the risk for ASF introduction. Subsequently, ASF experts were asked to rank those hypothetical districts in terms of the likelihood of serving as port of entry for the disease into the country.

An ordinal logistic regression model was run to estimate the relative weight that the experts implicitly gave to each of the variables, as approximated by the value of the regression coefficients. The regression coefficients were then validated using data from the Russian Federation and Mongolia. Finally, the model was used to project the risk in Kazakhstani districts, and high-risk clusters were identified using the spatial scan statistics, to help inform the regionalization of surveillance activities in the country.

A hypothetical ASF-free country was divided into 10 districts using a combination of epidemiological factors hypothesized to influence the risk for ASFv introduction. The 10 districts were designed so that eight of them were created using a factorial design to balance the distribution of epidemiological factors, and two of them represented the scenarios of best and worst possible combination of factors, in terms of their expected risk for the disease Table 1.

The selection of factors hypothesized to influence the risk was based on previous experience of the authors and supported by a literature search. Pig density, estimated wild boar density, and backyard farming were chosen with the objective of capturing the influence associated with the distribution of the susceptible population.

During the ASF outbreaks in Russian Federation, for example, pig population density was identified as an important risk factor for the disease 14 , Wild boars can also be responsible for transboundary ASF spread due to their natural dispersal ecology in search of new territory 7 , 13 , Swill feeding is considered a relatively common practice in many backyard farming systems, which, in addition to limited biosecurity in those types of farms, has been associated with a high risk for the disease 4 , Finally, human density and road density were included as a proxy for the movement of people, given that travelers can carry contaminated or infected goods and because ASFv can survive for extended periods of time in the environment and in pork products 18 , 21 Table 2.

Table 1. A hypothetical African swine fever ASF -free country was divided into 10 districts that were characterized in terms of the risk for an ASF introduction using a list of epidemiological factors hypothesized to influence the risk and a factorial design. Table 2. Epidemiological factors hypothesized to influence the risk for African swine fever ASF were categorized as dichotomous variables considering the values observed in selected countries and regions.

The four Reference Centers for ASF were asked to provide names for individuals that would have sufficient knowledge and experience to rank the hypothetical districts in terms for their risk for an ASFv introduction.

Snowball sampling 30 was used to designate experts, defined as those individuals that were mentioned at least by two reference centers. A list of 12 experts was identified and was invited to rank the 10 hypothetical districts in terms of the risk for an ASFv incursion, so that 1 and 10 denoted the districts with the lowest and highest risk of becoming ASF-infected, respectively.

A table with some definitions and reference values was provided to the experts for helping them understand the values that were used for categorizing the variables Table 2. An ordinal logistic regression OLR , proportional odds model was fitted to the answers provided by the experts so that. Variables were screened for collinearity prior to their introduction as candidate predictors in the model, and the final model was selected using Akaike's information criterion AIC.

Because Kazakhstan is a large country 9th largest in the world and because the size of the units at which data are aggregated may influence results, the five largest countries Poland, Germany, Ukraine, Mongolia, and the Russian Federation from the initial pool of fourteen were subsequently selected as candidate countries for validation. Values for the variables used as risk factors in the model were collected for the five countries at the subnational level and compared with those observed in Kazakhstan 23 , 35 — 37 Table 4.

Poland, Germany, and Ukraine were eliminated as candidate countries for the validation because they are substantially smaller ranking 69, 63, and 45 in globally size countries, respectively and also because of the differences in the distribution of values for all assessed variables compared to Kazakhstan—i. Subsequently, only Mongolia and the Russian Federation were considered adequate for the validation, even acknowledging the differences that exist between those countries and Kazakhstan.

Table 3. Table 4. For the validation, the regression coefficients obtained from the OLR model were used as weighting factors for the data collected in both Mongolia and the Russian Federation to identify the three districts regions or oblasts predicted to be at the highest risk for introduction of ASFv when those countries were free from the disease Table 5.

The results, which indicated the districts that would have been identified by our model and the expert opinion elicited here at the highest risk for ASFv introduction into the Russian Federation and Mongolia, were compared to the districts through which the disease was introduced into those countries when they first-became ASF-infected, as recorded by OIE's World Animal Health database WAHID 38 , Table 5.

Association between selected epidemiological factors and risk for introduction of African swine fever ASF into a free country located in an infected region, as suggested by elicitation of expert opinion through a conjoint analysis model.

Figure 1 depicts the categorization of these district-level data in Kazakhstan. The normal model of the spatial scan statistic has been described elsewhere Briefly, circles of variable radius are alternatively imposed over the centroids and candidate clusters, including groups of neighboring districts, are identified. The average risk for ASF introduction was computed for each candidate cluster and compared with the expected under the null hypothesis that all observations come from the same distribution.

Significance of the deviation of the observed risk, compared to the expected, was estimated for each candidate cluster using Monte Carlo simulation. Results for Kazakhstan were plotted in choropleth maps. Figure 1. Kazakhstan district-level data for the variables used in the model backyard farming share, domestic pig density, estimated wild boar density, share-border with ASF-infected country, human population density, and road density.

The SPSS software 41 was used for the factorial design of the 10 hypothetical districts. The RStudio Team version 3. The SaTScan v. ArcGIS The data collection process led to the registration of 2, pig farms throughout Kazakhstan. This categorization only reflects the legal property type, as no biosecurity-based classification is currently effective in Kazakhstan.

Figure 2. Distribution of swine farms in Kazakhstan. The location of swine operations is indicated and categorized as single-owner farms green dots and commercial association-owned blue dots farms. The factor that experts considered most important in driving the risk for introduction of ASFv into a free district was a high density of backyard farming, followed by high density of pigs and high estimated density of wild boars Table 5.

Despite that road and human densities were not significantly associated with the score provided by the experts, inclusion of those variables in the final model resulted in the lowest AIC value recorded for any combination of variables AIC: The three Russian Federation districts predicted to be at highest risk for introduction of the disease were the Republic of North Ossetia-Alania, Bryansk Oblast, and the Orenburg Oblast, respectively.

Although ASFv was first reported in Chechen Republic in November , which would not have been predicted by our model, the second massive incursion of ASFv into Russian Federation was reported in June in the Republic of North Ossetia-Alania, followed by cases in Orenburg Oblast in July , coincidentally with the model predictions. Coincidently, the three districts had the first occurrence of ASF in January High-risk clusters were located in the Almaty southern Kazakhstan and Kostanay northern Kazakhstan regions and include seven and nine districts, respectively Figure 3.

Figure 3. Risk for introduction of African swine fever ASF into Kazakhstan estimated using a conjoint analysis model. The map on the top A depicts districts grouped into four quantiles based on the predicted risk the darker the shade, the higher the risk , whereas the map on the bottom B illustrates the location of clusters of high risk for the introduction of ASF into the country detected using the normal model of the spatial scan statistic.

Following the fall of the Soviet Union and given that the majority of the population of Kazakhstan is Muslim, the number of swine operations in the country has substantially decreased. Furthermore, the geographical proximity of Kazakhstan with China has increased the country's interest in promoting the production of pork to supply the emerging demand in China associated with the ASF epidemic.

In order to protect the status of the Kazakh swine industry, it is critical to understand the distribution of the susceptible population and characterize the risks associated with disease status.

For the first time, we have conducted here a comprehensive survey of the distribution of swine farms in Kazakhstan, showing its selective concentration in the northern and southern regions of the country Figure 2.

The relative isolation of Kazakhstan, along with the small size of its pig industry, may have helped the country to avoid the introduction of ASFv, despite the unprecedented spread of the disease through Europe and Asia. However, given that a number of neighboring countries have become ASF-infected, there is a need for supporting Kazakhstan preparedness through the identification of areas at highest risk for ASFv introduction.

The results here may help to target surveillance activities to those districts identified at highest risk for disease introduction to increase the sensitivity of the national surveillance system and support the early detection of a hypothetical ASF introduction into the country Figure 3.



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Risk for African Swine Fever Introduction Into Kazakhstan

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