Modelling bluetongue risk in Kazakhstan.

BACKGROUND: Bluetongue is a serious disease of ruminants caused by the bluetongue virus (BTV). BTV is transmitted by biting midges (Culicoides spp.). Serological evidence from livestock and the presence of at least one competent vector species of Culicoides suggests that transmission of BTV is possible and may have occurred in Kazakhstan. METHODS: We estimated the risk of transmission using a mathematical model of the reproduction number R0 for bluetongue. This model depends on livestock density and climatic factors which affect vector density. Data on climate and livestock numbers from the 2466 local communities were used. This, together with previously published model parameters, was used to estimate R0 for each month of the year. We plotted the results on isopleth maps of Kazakhstan using interpolation to smooth the irregular data. We also mapped the estimated proportion of the population requiring vaccination to prevent outbreaks of bluetongue. RESULTS: The results suggest that transmission of bluetongue in Kazakhstan is not possible in the winter from October to March. Assuming there are vector-competent species of Culicoides endemic in Kazakhstan, then low levels of risk first appear in the south of Kazakhstan in April before spreading north and intensifying, reaching maximum levels in northern Kazakhstan in July. The risk declined in September and had disappeared by October. CONCLUSION: These results should aid in surveillance efforts for the detection and control of bluetongue in Kazakhstan by indicating where and when outbreaks of bluetongue are most likely to occur. The results also indicate where vaccination efforts should be focussed to prevent outbreaks of disease.

Risk for African Swine Fever Introduction Into Kazakhstan.

African swine fever (ASF) is a disease of swine that is endemic to some African countries and that has rapidly spread since 2007 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.

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.

Revealing spatio-temporal patterns of rabies spread among various categories of animals in the Republic of Kazakhstan, 2010-2013.

This study estimated the basic reproductive ratio of rabies at the population level in wild animals (foxes), farm animals (cattle, camels, horses, sheep) and what we classified as domestic animals (cats, dogs) in the Republic of Kazakhstan (RK). It also aimed at forecasting the possible number of new outbreaks in case of emergence of the disease in new territories. We considered cases of rabies in animals in RK from 2010 to 2013, recorded by regional veterinary services. Statistically significant space-time clusters of outbreaks in three subpopulations were detected by means of Kulldorff Scan statistics. Theoretical curves were then fitted to epidemiological data within each cluster assuming exponential initial growth, which was followed up by calculation of the basic reproductive ratio R0. For farm animals, the value of R0 was 1.62 (1.11-2.26) and for wild animals 1.84 (1.08- 3.13), while it was close to 1 for domestic animals. Using the values obtained, an initial phase of possible epidemic was simulated in order to predict the expected number of secondary cases if the disease were introduced into a new area. The possible number of new cases for 20 weeks was estimated at 5 (1-16) for farm animals, 17 (1-113) for wild animals and about 1 in the category of domestic animals. These results have been used to produce set of recommendations for organising of preventive and contra-epizootic measures against rabies expected to be applied by state veterinarian services.