Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vecto... more Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the
African swine fever virus (ASFV), classified as genotype II, was introduced into Georgia in 2007,... more 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 in...
African swine fever virus (ASFV), classified as genotype II, was introduced into Georgia in 2007,... more 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 in...
Modeling of the spatial distribution of favorable climatic conditions for transmission of dirofil... more Modeling of the spatial distribution of favorable climatic conditions for transmission of dirofilariasis in European part of Russia was carried out in order to identify the climatic parameters affecting its distribution. To build the model, the method of modeling ecological niches with optimization by the method of maximum entropy (MaxEnt) was used. The following parameters were considered: average annual air temperature, average annual precipitation, maximum temperature of the warmest month and minimum temperature of the coldest month. The most important climatic factor influencing the spread of dirofilariasis are thermal conditions (air temperature). The average annual rainfall has no significant effect. The most favorable territories of European part of Russia include the Black Earth Region, the Rostov Oblast, the Krasnodar Kray and the Crimea.
БОЛЕЗНИ СВИНЕЙ PORCINE DISEASES РЕЗЮМЕ Представлены результаты ретроспективного анализа эпизоотич... more БОЛЕЗНИ СВИНЕЙ PORCINE DISEASES РЕЗЮМЕ Представлены результаты ретроспективного анализа эпизоотической ситуации по африканской чуме свиней в Российской Федерации, включающего анализ хронологии развития эпизоотии, в котором выделены значимые события при распространении болезни по территории страны. Показан общий тренд и темп эпизоотии африканской чумы свиней 2007-2017 гг. на территории Евразии по числу пораженных стран (1,273 ± 1,272 страны/год) и субъектов Российской Федерации (4,5 ± 2,3 субъекта/год). Также в работе акцентируется внимание на некоторых особенностях эпизоотического процесса в зависимости от сезона года. Рассмотрены вероятные социальные и биологические факторы, способствующие распространению эпизоотии. Обсуждена необходимость дальнейших поисков вероятного биологического вектора и изу чения социальной составляющей, возможно способствующих поддержанию очагов болезни в дикой природе и среди домашних свиней на территории России и стран Восточной Европы. Ключевые слова: африканская чума свиней, домашние свиньи, дикие кабаны, эпизоотия, сезонность заболевания. SUMMARY Results of retrospective analysis of African swine fever epidemic situation in the Russian Federation including analysis of the epidemic chronology indicating significant events of the disease spread across the country territory are presented. Common ASF epidemic trend and rate in 2007-2017 in Eurasia territory based on number of infected countries (1.273 ± 1.272 countries/year) and in Russian Federation Subjects (4.5 ± 2.3 Subjects/year) are shown. The paper also addresses some peculiarities of the epidemic process depending on the season of a year. Possible social and biological factors contributing to the epidemic spread are examined. Necessity of further search for probable biological vector and examination of social factors that could contribute to the disease maintenance in wild life and in domestic pigs in the Russian Federation and Eastern European countries are discussed.
Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and... more Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016). The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus. Methods A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated. Results A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96). Conclusions Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas.
African swine fever is viral disease of domestic and wild pigs which leads to almost total mortal... more African swine fever is viral disease of domestic and wild pigs which leads to almost total mortality and causes great economic losses due to absence of vaccine. Having been introduced into the Russian Federation in 2007 the disease has spread widely in the southern region of the country and since 2011 has demonstrated a tendency to form a secondary endemic zone in the central part of the country. In the present study spatio-temporal patterns of ASF diffusion in the populations of wild and domestic pigs are analyzed. The structure of the domestic swine population is conventionally divided into a sub-population at low biosecurity (77% of the total number of outbreaks in domestic pigs) and a population at high biosecurity (23%). The statistics of ASF cases registered in each of these sub-populations is presented. The possible causes of ASF diffusion across the country are discussed. The use of geo-information technologies (GIS) enabled confirmation of the conclusion that an epidemic center has shifted into the central part of Russia. The main conclusions of this study are that: (1) anthropogenic factors play the leading role in the spread of ASF across the territory of the RF; (2) small-scale private holdings (low biosecurity population) are more exposed to ASF virus introduction; (3) there is a high risk of diffusion of ASFV from the secondary endemic zone in the central part of the RF to neighboring regions.
The paper presents the genetic characteristics of two strains of spring viraemia of carp virus (S... more The paper presents the genetic characteristics of two strains of spring viraemia of carp virus (SVCV), Kirov/08 and Orenburg/14, isolated in the Kirov and Orenburg oblasts of the Russian Federation, respectively. Nucleotide sequence analysis of a 516-bp fragment of the G genes of Kirov/08 and Orenburg/14 showed 9.5% divergence. Phylogenetic analysis demonstrated a close relationship of these strains to SVCV isolates recovered in Russia, Ukraine, and the Republic of Moldova.
Rabies is endemic to the Russian Federation. The disease incidence ranges between 2,000 and 4,000... more Rabies is endemic to the Russian Federation. The disease incidence ranges between 2,000 and 4,000 cases annually. Between two and six cases in humans are also reported each year. Wild animals are the basic reservoir and vector of the virus, and the incidence of rabies in foxes and raccoon dogs amounts to 50% of the total number of disease cases. When outbreaks occur, the disease is also reported in domestic animals. To prevent the further spread of rabies, vaccination of domestic animals and oral immunisation of wildlife are practised. Unfortunately, vaccine coverage and disease prevention measures have not been sufficient to achieve a notable improvement in the rabies situation in the country.
The present review examines the long-term trend of declining populations of "wild" Atla... more The present review examines the long-term trend of declining populations of "wild" Atlantic salmon in most European coun-tries and north-western regions of Russia, due to intensive fishing, poaching, anthropogenic impact on marine waters and freshwa-ter river systems, logging, numerous dams and hydroelectric power plants on spawning rivers, destruction of aquatic biotopes and bottom biocenoses. Deep depression of the majority of Atlantic salmon populations has been observed in the Arctic fishing re-gions of the Russian Federation for more than 30 years, which has led to a decrease in average annual catches by more than 20 times over half a century. Since 2015, the Federal Center for Animal Health has been conducting research to identify the causes of viral and bacterial etiology diseases and the death of Atlantic salmon populations going to spawn in the rivers of the Kola Pen-insula. Currently, in the course of research, the causes of disease and death of Atlantic salmon h...
In this study, we simulated the potential spread of Peste des Petits Ruminants (PPR) between smal... more 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 rath...
Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vecto... more Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the
African swine fever virus (ASFV), classified as genotype II, was introduced into Georgia in 2007,... more 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 in...
African swine fever virus (ASFV), classified as genotype II, was introduced into Georgia in 2007,... more 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 in...
Modeling of the spatial distribution of favorable climatic conditions for transmission of dirofil... more Modeling of the spatial distribution of favorable climatic conditions for transmission of dirofilariasis in European part of Russia was carried out in order to identify the climatic parameters affecting its distribution. To build the model, the method of modeling ecological niches with optimization by the method of maximum entropy (MaxEnt) was used. The following parameters were considered: average annual air temperature, average annual precipitation, maximum temperature of the warmest month and minimum temperature of the coldest month. The most important climatic factor influencing the spread of dirofilariasis are thermal conditions (air temperature). The average annual rainfall has no significant effect. The most favorable territories of European part of Russia include the Black Earth Region, the Rostov Oblast, the Krasnodar Kray and the Crimea.
БОЛЕЗНИ СВИНЕЙ PORCINE DISEASES РЕЗЮМЕ Представлены результаты ретроспективного анализа эпизоотич... more БОЛЕЗНИ СВИНЕЙ PORCINE DISEASES РЕЗЮМЕ Представлены результаты ретроспективного анализа эпизоотической ситуации по африканской чуме свиней в Российской Федерации, включающего анализ хронологии развития эпизоотии, в котором выделены значимые события при распространении болезни по территории страны. Показан общий тренд и темп эпизоотии африканской чумы свиней 2007-2017 гг. на территории Евразии по числу пораженных стран (1,273 ± 1,272 страны/год) и субъектов Российской Федерации (4,5 ± 2,3 субъекта/год). Также в работе акцентируется внимание на некоторых особенностях эпизоотического процесса в зависимости от сезона года. Рассмотрены вероятные социальные и биологические факторы, способствующие распространению эпизоотии. Обсуждена необходимость дальнейших поисков вероятного биологического вектора и изу чения социальной составляющей, возможно способствующих поддержанию очагов болезни в дикой природе и среди домашних свиней на территории России и стран Восточной Европы. Ключевые слова: африканская чума свиней, домашние свиньи, дикие кабаны, эпизоотия, сезонность заболевания. SUMMARY Results of retrospective analysis of African swine fever epidemic situation in the Russian Federation including analysis of the epidemic chronology indicating significant events of the disease spread across the country territory are presented. Common ASF epidemic trend and rate in 2007-2017 in Eurasia territory based on number of infected countries (1.273 ± 1.272 countries/year) and in Russian Federation Subjects (4.5 ± 2.3 Subjects/year) are shown. The paper also addresses some peculiarities of the epidemic process depending on the season of a year. Possible social and biological factors contributing to the epidemic spread are examined. Necessity of further search for probable biological vector and examination of social factors that could contribute to the disease maintenance in wild life and in domestic pigs in the Russian Federation and Eastern European countries are discussed.
Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and... more Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016). The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus. Methods A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated. Results A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96). Conclusions Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas.
African swine fever is viral disease of domestic and wild pigs which leads to almost total mortal... more African swine fever is viral disease of domestic and wild pigs which leads to almost total mortality and causes great economic losses due to absence of vaccine. Having been introduced into the Russian Federation in 2007 the disease has spread widely in the southern region of the country and since 2011 has demonstrated a tendency to form a secondary endemic zone in the central part of the country. In the present study spatio-temporal patterns of ASF diffusion in the populations of wild and domestic pigs are analyzed. The structure of the domestic swine population is conventionally divided into a sub-population at low biosecurity (77% of the total number of outbreaks in domestic pigs) and a population at high biosecurity (23%). The statistics of ASF cases registered in each of these sub-populations is presented. The possible causes of ASF diffusion across the country are discussed. The use of geo-information technologies (GIS) enabled confirmation of the conclusion that an epidemic center has shifted into the central part of Russia. The main conclusions of this study are that: (1) anthropogenic factors play the leading role in the spread of ASF across the territory of the RF; (2) small-scale private holdings (low biosecurity population) are more exposed to ASF virus introduction; (3) there is a high risk of diffusion of ASFV from the secondary endemic zone in the central part of the RF to neighboring regions.
The paper presents the genetic characteristics of two strains of spring viraemia of carp virus (S... more The paper presents the genetic characteristics of two strains of spring viraemia of carp virus (SVCV), Kirov/08 and Orenburg/14, isolated in the Kirov and Orenburg oblasts of the Russian Federation, respectively. Nucleotide sequence analysis of a 516-bp fragment of the G genes of Kirov/08 and Orenburg/14 showed 9.5% divergence. Phylogenetic analysis demonstrated a close relationship of these strains to SVCV isolates recovered in Russia, Ukraine, and the Republic of Moldova.
Rabies is endemic to the Russian Federation. The disease incidence ranges between 2,000 and 4,000... more Rabies is endemic to the Russian Federation. The disease incidence ranges between 2,000 and 4,000 cases annually. Between two and six cases in humans are also reported each year. Wild animals are the basic reservoir and vector of the virus, and the incidence of rabies in foxes and raccoon dogs amounts to 50% of the total number of disease cases. When outbreaks occur, the disease is also reported in domestic animals. To prevent the further spread of rabies, vaccination of domestic animals and oral immunisation of wildlife are practised. Unfortunately, vaccine coverage and disease prevention measures have not been sufficient to achieve a notable improvement in the rabies situation in the country.
The present review examines the long-term trend of declining populations of "wild" Atla... more The present review examines the long-term trend of declining populations of "wild" Atlantic salmon in most European coun-tries and north-western regions of Russia, due to intensive fishing, poaching, anthropogenic impact on marine waters and freshwa-ter river systems, logging, numerous dams and hydroelectric power plants on spawning rivers, destruction of aquatic biotopes and bottom biocenoses. Deep depression of the majority of Atlantic salmon populations has been observed in the Arctic fishing re-gions of the Russian Federation for more than 30 years, which has led to a decrease in average annual catches by more than 20 times over half a century. Since 2015, the Federal Center for Animal Health has been conducting research to identify the causes of viral and bacterial etiology diseases and the death of Atlantic salmon populations going to spawn in the rivers of the Kola Pen-insula. Currently, in the course of research, the causes of disease and death of Atlantic salmon h...
In this study, we simulated the potential spread of Peste des Petits Ruminants (PPR) between smal... more 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 rath...
Lumpy Skin Disease (LSD) is a viral infectious disease of cattle and buffalo causing a huge econo... more Lumpy Skin Disease (LSD) is a viral infectious disease of cattle and buffalo causing a huge economic impact due to loss of productivity of infected animals. Being first introduced in Eurasia in 1989 with a consequent wide transboundary transmission since 2012, the disease is believed to primarily spread by arthropod vector. Hence, a potential range of the disease within an affected territory may be determined by environmental suitability for the vector habitat. The goal of the current study was to map the potential area within Eurasia where the LSD can be transmitted by its vector, and to assess the expected change of the area by 2060 using a projected climate data. The modeling was based on the BAM concept, where a potential ecological niche of a species is defined as an intersection of Biotic and Abiotic factors as well as an ability of the species to Migrate within the available area. The entire area of Eurasia with a cattle density higher than 0.5 heads/km2 was chosen as a model area (M), while abiotic factors were presented by a set of bioclimatic variables describing peak and trend values of the air temperature and precipitation within the study area. The maximum entropy-based algorithm (Maxent) was applied to obtain a probability surface indicating an environmental suitability to LSD vector habitat using known LSD cases in Eurasia 2012 – 2020 as the presence locations. After calibration with the current climate data, Maxent model was projected with bioclimatic variables recalculated using the climate model INMCM4 for a ‘mild’ (RCP4.5) and ‘tough’ (RCP8.5) scenarios for 2041 – 2060. The results suggest a high suitability of the large territories of the South-West Europe, Mediterranean region and Central Asia for the LSD transmission. An expected climate change by a “mild” scenario may cause a strongest increase of the suitability in the Central Asia affecting the large areas of Kazakhstan and Russia, as well as in South East Asia. A “tough” climate change scenario may lead to a less pronounced expansion of the disease range in the Central Asia, while large territories of East Asia (Far East of Russia, eastern parts of Mongolia and China) are expected to become more suitable for the disease vector. Results of the study may contribute to the improvement of the current LSD preventive strategies by providing mapping background for the targeted surveillance and vaccination campaigns.
1st IALE-Russia online International Conference LANDSCAPE SCIENCE AND LANDSCAPE ECOLOGY: CONSIDERING RESPONSES TO GLOBAL CHALLENGES, 2020
We present the study on assessment of the potential suitability of the Arctic territories of the ... more We present the study on assessment of the potential suitability of the Arctic territories of the Russian Federation to re-emergence of anthrax in humans and animals due to re-activation of preserved soil foci under the current climate as well as for the projected climate by 2100. The study is based on the assumption that the existing locations of anthrax burial sites may be treated as indicators of favorable environmental conditions for anthrax pathogen survival and, hence, all areas with a similar set of environmental parameters may be detected and categorized as potentially dangerous. We used the data on anthrax burial sites in Arctic region the Russia (N = 357) for 1882 – 2016 collected from publications of the Ministry of Agriculture. For modelling, a method from a category of Environmental Niche Modeling methods was applied, namely “a modeling algorithm with maximum entropy principle-based optimization” (or just MaxEnt). A number of environmental factors, such as soil type and pH, altitude above sea level, vegetation cover type and a set of climatic parameters were used as input explanatory factors. Maxent model demonstrated a good predictive ability (AUC = 0.969). A map was created that shows the most suitable areas for anthrax re-emergence in the south of Arkhangelsk oblast and Republic of Karelia, north of Murmansk oblast, south of Krasnoyarsk krai and central parts of the Republic of Sakha (Yakutia). The following variables were found to most contribute into the model demonstrating good correspondence to the findings of previous studies: soil pH, annual mean temperature, soil type, vegetation cover type, annual precipitation at temperatures below 0°C and maximum average daily air temperature. Using the set of climate variables projected with CMIP5 climate models ensemble until 2100 under the RCP8.5 scenario we obtained a suitability map for future climate that suggests a dramatic increase of suitability over the whole study area. A particularly strong rise of suitability can be noted at the Yamal peninsula and along coastal areas of Yamal-Nenets autonomous district, Republic of Sakha (Yakutia) and Chukotka autonomous district. The results of our study attract attention to the expected elevation of anthrax burials re-activation risks due to thawing permafrost that requires enhanced surveillance measures and proper containment of registered burial sites.
The study is a part of the Research Project 18-05-60037 by Russian Foundation for Basic Researches.
Spatio-temporal analysis of the local African swine fever epidemics in the Russian Federation, 2007 -2015, 2019
Background and objectives
African swine fever (ASF) is a contagious viral disease of domestic and... more Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016). The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus.
Methods A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated.
Results A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96).
Conclusions Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas.
African swine fever (ASF) is the viral disease of domestic and wild pigs that can lead up to 100%... more African swine fever (ASF) is the viral disease of domestic and wild pigs that can lead up to 100% mortality among affected animals and it causes huge economic impact. After its introduction into the Russian Federation in 2007 the disease spread widely and formed two spatio-temporal clusters. The model is developed to describe the epidemic on the basis of probabilistic approach, which includes: 1) Maximum Entropy suitability analysis of the territory by the aggregation of geographical and socio-economic factors and 2) statistical analysis of distances and time intervals between the ASF cases reported in 2007 – 2012. Modeling was based on the data on ASF outbreaks in domestic pigs from the largest cluster. MaxEnt software was used for suitability analysis that allows to reveal the associative relations between localization of ASF cases and risk factors, such as swine population density, human population density, settlements density, roads density. Statistical analysis and distributions fitting were performed by means of @Risk software package. Statistical analysis of distances distribution shows the mean distance between two subsequent ASF outbreaks is 156 km (95% CI: 0 – 467). The average time interval between subsequent outbreaks is 8 days (0 – 23). These parameters allow to create an exponential probability surface around an ASF outbreak location, which reflects the risk of the next case emergence within the MaxEnt suitability surface. The main conclusions are as follows: a) the leading factor of ASF virus spread is transportation of contaminated animal products from the focus of infection; b) the established size of risk zone of 20 km around the focus of infection is insufficient for effective control of the disease.
African swine fever is the viral disease of pigs that can lead up to 100% mortality among infecte... more African swine fever is the viral disease of pigs that can lead up to 100% mortality among infected animals and causes huge economic losses in the Russian Federation. The space-time analysis of the disease spread conducted by means of ArcGIS tools allowed to develop a model that detects a high risk area of the infection diffusion from an affected site. Recommendations for veterinary services were proposed to improve preventive measures by adjusting sizes and shapes of surveillance zones.
African swine fever (ASF) is a viral disease of domestic and wild pigs which leads to almost tota... more African swine fever (ASF) is a viral disease of domestic and wild pigs which leads to almost total mortality among the affected animals. Due to the absence of the vaccine the only measure to prevent spread of the disease is total slaughter of livestock population in the focus of infection and depopulation within the risk zone. The necessary measure is restriction of transportation and trading of animals and pork products. More than 280 ASF outbreaks have been reported from private holdings, large pig-breeding farms and in wild boars population in the territory of the Russian Federation since 2007. The analysis of spatial distribution of ASF cases performed by means of GIS-technologies demonstrates a correlation between the disease spread and direction of primary transportation links, domestic swine population density and human population density. This allows us to make a conclusion about predominately anthropogenic nature of the virus transmission by transportation and trade of live animals and contaminated agricultural products. MaxEnt software modeling with regard to detected risk factors allowed us to create a risk map which indicates the probability of emergence and spread of the disease in different regions of the European part of the country. The evaluation of economic losses caused by ASF in 2010-2011 was performed using the methodology adopted by the OIE considering both direct impact to livestock sector (costs of animal slaughter in the focus of infection and depopulation in the risk zone, losses of processing industry, indemnity payments to owners) and indirect losses associated with transportation and touristic restrictions, feed and grain trading bans etc. According to experts’ opinion the total economic damage was 2271 million Rubles (about 57 million Euros) in 2010.
Introduction
This research is devoted to the estimation of basic reproductive ratio (R0) of Afric... more Introduction This research is devoted to the estimation of basic reproductive ratio (R0) of African swine fever (ASF) in domestic pigs at within- as well as between-holdings level. Recently published detailed data on pigs’ mortality during the ASF outbreak in Odessa region of the Ukraine in 1977 were used for estimation of R0. The epidemic has affected 20 settlements, in which domestic pigs were infected both at large farms and at private backyards. Timely application of control measures allowed successful eradication of the epidemic and prevention of further spread of the virus.
Methods The method of early exponential-growth rate based on the analysis of an epidemic curve was chosen for estimation of R0. The course of epidemic within farm at Usatovo village demonstrates three epidemic waves that could be caused by consecutive infection of pigs from one pigpen to another. An exponential curve was fitted to the initial phase of each wave followed by calculation of R0 by simulation of infection period’s duration using Monte-Carlo method. Similarly, an exponential curve was fitted to the incidence data at between-farm level. The period of time from the date of symptoms onset at the farm to the date of confirmation of diagnosis and applying quarantine measures was taken as an infectious period.
Results The modeling estimates a basic reproductive ratio within an infected farm as 5.9 (2.3 – 12.8), and at between-farm level as 2.0 (1.1 – 4.9). The values are consistent with the results obtained by other authors.
Discussion Detailed data on natural dynamics of mortality, particularly within affected farm, are rarely available and, thus, are especially valuable. Results of the work have importance for understanding of the ASF epidemic process from practical point of view since they can help preventing the spread of the disease at different levels more effectively.
The applied usage of GIS in epidemiology means the mapping of information on the epidemic situa-t... more The applied usage of GIS in epidemiology means the mapping of information on the epidemic situa-tion in a particular territory against a set of topical layers (i.e. animal and human population, transportation network, water bodies, vegetation etc.). The availability of geospatial information helps solve one of the principal problems in epidemiology: it allows discovering the causes that contribute to the occurrence and spread of emerging infections in a population, since such causes may be closely connected with both environmental and socio-economic factors of certain localization. Here we consider the application of some spatial analysis functions of ArcMap (Esri, USA) by the example of diseases under study in the Federal Centre of Animal Health. The basic instruments of geostatistical analysis that are used in the practice of epidemiological analysis are the calculation of Mean Centre allowing to estimate the tendency of disease-front advance in time; and Standard Deviational Ellipse that allows to define the main direction of the disease spread. The simplest GIS tool that allows estimating the tendency towards the cases clustering is the Nearest Neighbor Index. Other program instruments that are also frequently used are Moran’s I Statistic and Getis-Ord Statistic, based on which it is possible to visualize the existing cases’ clusters. Multi-Distance Cluster Analysis: Ripley K Function is another useful tool for the estimation of case clustering at different distances from the mean center. To define the correlation of disease cases with geographical and socio-economic factors in the study area, a methodology of Associative Analysis is applied. This methodology means the application of a regression model that correlates the Spatial Density of cases’ distribution within the territory with the values of the selected geospatial and socio-economic variables. Based on the obtained regression equation, a risk map can be created that illustrates the probability of emergence of the disease within the study area. One of the novel methods that are used to detect the habitat of a certain host/causative agent/parasite species is the Maximum Entropy Modeling. MaxEnt software (Princeton University, USA) allows to carry out a complex analysis of habitat factors and to create a probability map which illustrates suitable conditions for the habitation of a particular species. We suggest using MaxEnt software for the analysis of possible emergence of disease cases in the territory of study due to an aggregate of geographical and socio-economic factors. This method was used for analyzing of African swine fever cases distribution in the territory of Russian Federation.
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Papers by Fedor Korennoy
The modeling was based on the BAM concept, where a potential ecological niche of a species is defined as an intersection of Biotic and Abiotic factors as well as an ability of the species to Migrate within the available area. The entire area of Eurasia with a cattle density higher than 0.5 heads/km2 was chosen as a model area (M), while abiotic factors were presented by a set of bioclimatic variables describing peak and trend values of the air temperature and precipitation within the study area. The maximum entropy-based algorithm (Maxent) was applied to obtain a probability surface indicating an environmental suitability to LSD vector habitat using known LSD cases in Eurasia 2012 – 2020 as the presence locations. After calibration with the current climate data, Maxent model was projected with bioclimatic variables recalculated using the climate model INMCM4 for a ‘mild’ (RCP4.5) and ‘tough’ (RCP8.5) scenarios for 2041 – 2060.
The results suggest a high suitability of the large territories of the South-West Europe, Mediterranean region and Central Asia for the LSD transmission. An expected climate change by a “mild” scenario may cause a strongest increase of the suitability in the Central Asia affecting the large areas of Kazakhstan and Russia, as well as in South East Asia. A “tough” climate change scenario may lead to a less pronounced expansion of the disease range in the Central Asia, while large territories of East Asia (Far East of Russia, eastern parts of Mongolia and China) are expected to become more suitable for the disease vector.
Results of the study may contribute to the improvement of the current LSD preventive strategies by providing mapping background for the targeted surveillance and vaccination campaigns.
The study is a part of the Research Project 18-05-60037 by Russian Foundation for Basic Researches.
African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016).
The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus.
Methods
A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated.
Results
A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96).
Conclusions
Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas.
Modeling was based on the data on ASF outbreaks in domestic pigs from the largest cluster. MaxEnt software was used for suitability analysis that allows to reveal the associative relations between localization of ASF cases and risk factors, such as swine population density, human population density, settlements density, roads density. Statistical analysis and distributions fitting were performed by means of @Risk software package.
Statistical analysis of distances distribution shows the mean distance between two subsequent ASF outbreaks is 156 km (95% CI: 0 – 467). The average time interval between subsequent outbreaks is 8 days (0 – 23). These parameters allow to create an exponential probability surface around an ASF outbreak location, which reflects the risk of the next case emergence within the MaxEnt suitability surface.
The main conclusions are as follows: a) the leading factor of ASF virus spread is transportation of contaminated animal products from the focus of infection; b) the established size of risk zone of 20 km around the focus of infection is insufficient for effective control of the disease.
More than 280 ASF outbreaks have been reported from private holdings, large pig-breeding farms and in wild boars population in the territory of the Russian Federation since 2007. The analysis of spatial distribution of ASF cases performed by means of GIS-technologies demonstrates a correlation between the disease spread and direction of primary transportation links, domestic swine population density and human population density. This allows us to make a conclusion about predominately anthropogenic nature of the virus transmission by transportation and trade of live animals and contaminated agricultural products. MaxEnt software modeling with regard to detected risk factors allowed us to create a risk map which indicates the probability of emergence and spread of the disease in different regions of the European part of the country.
The evaluation of economic losses caused by ASF in 2010-2011 was performed using the methodology adopted by the OIE considering both direct impact to livestock sector (costs of animal slaughter in the focus of infection and depopulation in the risk zone, losses of processing industry, indemnity payments to owners) and indirect losses associated with transportation and touristic restrictions, feed and grain trading bans etc. According to experts’ opinion the total economic damage was 2271 million Rubles (about 57 million Euros) in 2010.
This research is devoted to the estimation of basic reproductive ratio (R0) of African swine fever (ASF) in domestic pigs at within- as well as between-holdings level. Recently published detailed data on pigs’ mortality during the ASF outbreak in Odessa region of the Ukraine in 1977 were used for estimation of R0. The epidemic has affected 20 settlements, in which domestic pigs were infected both at large farms and at private backyards. Timely application of control measures allowed successful eradication of the epidemic and prevention of further spread of the virus.
Methods
The method of early exponential-growth rate based on the analysis of an epidemic curve was chosen for estimation of R0. The course of epidemic within farm at Usatovo village demonstrates three epidemic waves that could be caused by consecutive infection of pigs from one pigpen to another. An exponential curve was fitted to the initial phase of each wave followed by calculation of R0 by simulation of infection period’s duration using Monte-Carlo method. Similarly, an exponential curve was fitted to the incidence data at between-farm level. The period of time from the date of symptoms onset at the farm to the date of confirmation of diagnosis and applying quarantine measures was taken as an infectious period.
Results
The modeling estimates a basic reproductive ratio within an infected farm as 5.9 (2.3 – 12.8), and at between-farm level as 2.0 (1.1 – 4.9). The values are consistent with the results obtained by other authors.
Discussion
Detailed data on natural dynamics of mortality, particularly within affected farm, are rarely available and, thus, are especially valuable. Results of the work have importance for understanding of the ASF epidemic process from practical point of view since they can help preventing the spread of the disease at different levels more effectively.
The basic instruments of geostatistical analysis that are used in the practice of epidemiological analysis are the calculation of Mean Centre allowing to estimate the tendency of disease-front advance in time; and Standard Deviational Ellipse that allows to define the main direction of the disease spread.
The simplest GIS tool that allows estimating the tendency towards the cases clustering is the Nearest Neighbor Index. Other program instruments that are also frequently used are Moran’s I Statistic and Getis-Ord Statistic, based on which it is possible to visualize the existing cases’ clusters. Multi-Distance Cluster Analysis: Ripley K Function is another useful tool for the estimation of case clustering at different distances from the mean center.
To define the correlation of disease cases with geographical and socio-economic factors in the study area, a methodology of Associative Analysis is applied. This methodology means the application of a regression model that correlates the Spatial Density of cases’ distribution within the territory with the values of the selected geospatial and socio-economic variables. Based on the obtained regression equation, a risk map can be created that illustrates the probability of emergence of the disease within the study area.
One of the novel methods that are used to detect the habitat of a certain host/causative agent/parasite species is the Maximum Entropy Modeling. MaxEnt software (Princeton University, USA) allows to carry out a complex analysis of habitat factors and to create a probability map which illustrates suitable conditions for the habitation of a particular species. We suggest using MaxEnt software for the analysis of possible emergence of disease cases in the territory of study due to an aggregate of geographical and socio-economic factors. This method was used for analyzing of African swine fever cases distribution in the territory of Russian Federation.