Epidemiological analyses on African swine fever in the Baltic countries and Poland
2017; Wiley; Volume: 15; Issue: 3 Linguagem: Inglês
10.2903/j.efsa.2017.4732
ISSN1831-4732
AutoresJosé Cortiñas Abrahantes, Gogin Andrey, Jane Richardson, Andrea Gervelmeyer,
Tópico(s)T-cell and Retrovirus Studies
ResumoEFSA JournalVolume 15, Issue 3 e04732 Scientific ReportOpen Access Epidemiological analyses on African swine fever in the Baltic countries and Poland European Food Safety Authority (EFSA), European Food Safety Authority (EFSA)Search for more papers by this authorJosé Cortiñas Abrahantes, José Cortiñas AbrahantesSearch for more papers by this authorAndrey Gogin, Andrey GoginSearch for more papers by this authorJane Richardson, Jane RichardsonSearch for more papers by this authorAndrea Gervelmeyer, Andrea GervelmeyerSearch for more papers by this author European Food Safety Authority (EFSA), European Food Safety Authority (EFSA)Search for more papers by this authorJosé Cortiñas Abrahantes, José Cortiñas AbrahantesSearch for more papers by this authorAndrey Gogin, Andrey GoginSearch for more papers by this authorJane Richardson, Jane RichardsonSearch for more papers by this authorAndrea Gervelmeyer, Andrea GervelmeyerSearch for more papers by this author First published: 23 March 2017 https://doi.org/10.2903/j.efsa.2017.4732Citations: 24 Correspondence: alpha@efsa.europa.eu Requestor: European Commission Question number: EFSA-Q-2016-00152 Acknowledgements: EFSA wishes to thank the following for the support provided to this scientific report: Arvo Viltrop, Kärt Jaarma, Katrin Lõhmus, Imbi Nurmoja, Andrzej Kowalczyk, Łukasz Bocian, Gediminas Pridotkas, Zydrunas Vaisvila, Rimvydas Falkauskas, Marius Judickas, Ieva Rodze, Edvīns Oļševskis, Daina Pūle, Mārtiņš Seržants, Vittorio Guberti, Machteld Varewyck, Giuseppe Stancanelli, Roberta Palumbo, Gabriele Zancarano, Sofie Dhollander, Ewelina Czwienczek, Beatriz Beltran-Beck. EFSA wishes to acknowledge all European competent institutions, Member State bodies and other organisations that provided data for this scientific report. Adopted: 9 February 2017 Reproduction of the images listed below is prohibited and permission must be sought directly from the copyright holder: Figure 1: © European Commission, World Organisation for Animal Health (OIE), Federal Service for Veterinary and Phytosanitary Surveillance of Russia; Figure C.1: © Ministry of the Environment (Estonia); Figure C.2: © State Forest Service of Latvia; Figure C.3: © General Directorate of the State Forests (Poland) AboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract African swine fever virus (ASFV) has been notified in the Baltic countries and the eastern part of Poland from the beginning of 2014 up to now. In collaboration with the ASF-affected Member States (MS), EFSA is updating the epidemiological analysis of ASF in the European Union which was carried out in 2015. For this purpose, the latest epidemiological and laboratory data were analysed in order to identify the spatial–temporal pattern of the epidemic and a risk factors facilitating its spread. Currently, the ASF outbreaks in wild boar in the Baltic countries and Poland can be defined as a small-scale epidemic with a slow average spatial spread in wild boar subpopulations (approximately from 1 in Lithuania and Poland to 2 km/month in Estonia and Latvia). The number of positive samples in hunted wild boar peaks in winter which can be explained by human activity patterns (significant hunting activity over winter). The number of positive samples in wild boar found dead peaks in summer. This could be related to the epidemiology of the disease and/or the biology of wild boar; however, this needs further investigation. Virus prevalence in hunted wild boar is very low (0.04–3%), without any apparent trend over time. Apparent virus prevalence at country level in wild boar found dead in affected countries ranges from 60% to 86%, with the exception of Poland, where values between 0.5% and 1.42%, were observed. Since the beginning of the epidemic, the apparent antibody prevalence in hunted wild boar has always been lower than the apparent virus prevalence, indicating an unchanged epidemiological/immunological situation. The risk factor analysis shows an association between the number of settlements, human and domestic pigs population size or wild boar population density and the presence of ASF in wild boar for Estonia, Latvia and Lithuania. Summary In mid-February 2016, the European Food Safety Authority (EFSA) was requested to assist the European Commission and the Member States (MS) by collecting and analysing African swine fever (ASF) epidemiological data from the MS affected by ASF at the Eastern border of the European Union (EU) in the context of Article 31 of Regulation (EC) No 178/2002. To harmonise the collection of data from laboratory testing for ASF, the affected MS and EFSA developed a common data model in the EFSA Data Collection Framework (DCF), which collects sample and individual animal level data, from positive and as well as negative test results. For each record, the location of sampling, the age and sex of the sampled animal (or carcass), the matrixes tested and the diagnostic methods used can be recorded. Temporal trends of apparent virus (polymerase chain reaction (PCR)) and antibody prevalences were assessed using statistical models. For this purpose, data from laboratory testing for ASF submitted by the MS through the DCF, and data submitted in accordance with Council Directive 82/894/EEC to the EU Animal Disease Notification System (ADNS), were used. To estimate if the probability of the presence of ASFV in the wild boar population depends on a potential relationship between environmental and biological factors (i.e. risk factors), a logistic binary model/classification trees were used, which results in a saturated tree. The variable importance measure used was based on the prune tree (Breiman et al., 1984). In addition to the data provided by the MS, geographical data (land cover, density of roads and settlements) and population data (human population, domestic pig and wild boar population) were used. The analyses show that ASF spreads through the continuous wild boar population habitat of the four MS of Eastern Europe, and demonstrate an epidemic pattern with two peaks of notifications, in winter and summer. Analysis of spatio-temporal data shows that previously and newly established clusters of the disease in wild boar subpopulations are expanding, and that the average spatial spread of the disease in wild boar subpopulations in Latvia and Estonia is approximately 2 km/month, while in Lithuania and Poland the average spatial spread of the disease is approximately 1 km/month. This indicates a slow spread in the region. Temporal trends of apparent virus (PCR) and antibody prevalences in hunted wild boar for the period from January 2014 until August 2016 were assessed using a statistical model with a smooth-time component and revealed that the apparent virus prevalence is increasing in hunted wild boar in Estonia and Latvia. The number of positive samples in hunted wild boar peaks in winter. This winter increase is probably explained by human activity patterns (significant hunting activity over winter). The number of positive samples in wild boar found dead peaks in summer. This could be related to the epidemiology of the disease and/or the biology of wild boar; however, this needs further investigation. Virus prevalence in hunted wild boar is very low with apparent prevalence values ranging between 0.5% and 3%, without any apparent trend over time. Apparent virus prevalence in wild boar found dead in Estonia, Latvia and Lithuania ranges from 60% to 86%, with the exception of Poland, where values between 0.04% and 1.42% were observed. Since the beginning of the epidemic, the apparent antibody prevalence in hunted wild boar has always been lower than the apparent virus prevalence in hunted wild boar, indicating an unchanged epidemiological/immunological situation. Not all laboratory records of 2014–2015 contain information for all variables foreseen in the harmonised data model (e.g. exact location of sampling, carcass decomposition rate). For this reason, the analysis of relationships between of ASFV detections and the characteristics of the infected wild boar subpopulations and matrices (e.g. age and sex groups of animals, rate of decomposition of carcasses) is limited so far. An analysis of environmental and biological risk factors potentially involved in the occurrence of ASFV in the wild boar population showed that the association of these factors with the presence of ASFV differs between the years. The risk factor analysis shows an association between the number of settlements, the human population size as well as the number of domestic pigs and pig farms, roads, forest cover percentage and the presence of ASF in wild boar for Estonia, Latvia and Lithuania. The observed association of ASF presence with human population size, domestic pigs and pig farms might be an indicator of an involvement of humans in the spread of the disease; however, this association could also be explained by a higher probability to detect dead wild boar and to test samples for ASF in the vicinity of human populations and pig farms. Wild boar density was not identified as a potential risk factor associated with the presence of ASF in a region for all countries under consideration. Only for Estonia, the spatial–temporal statistics model results indicate that in 2014–2016 wild board density is proportionally related to the likelihood of observing ASF cases in a region. For Poland, no analysis of potential risk factors is presented due to limited information available. Looking at the Baltic countries, the model results indicate that the number of settlements, human and domestic pigs population size, and the percentage of forest cover are the potential influential factors for ASF cases in wild boar for the year 2016. Web-based tools for statistical data analysis developed by EFSA and the large data set containing different types of covariates such as environmental and demographic data, and harmonised data from MS's laboratory information management systems (LIMS) allow a comprehensive epidemiological analysis that can help to provide an adequate regionalisation and to develop targeted preventive measures. EFSA continues to provide full technical and methodological support to the MS through further collection and analysis of data. 1 Introduction Currently available data (Animal Disease Notification System (ADNS), World Animal Health Information System (WAHIS1), Official web site of the Federal Service for Veterinary and Phytosanitary Surveillance of the Russian Federation2) demonstrate that African swine fever (ASF) is spreading in the Eastern European region, which includes the Russian Federation, Ukraine and Moldova. The ASF situation in Eastern Europe up to the end of August 2016 is presented below in Figure 1. Figure 1Open in figure viewerPowerPoint Notifications of ASF in the Eastern Europe region in 2007–2016 Sources: ADNS, WAHIS, Official web site of the Federal Service for Veterinary and Phytosanitary Surveillance of Russia; period covered 1 January 2007–31 August 2016. The situation on ASF in Belarus remains unclear. There were no official notifications since 2013. In 2016, the epizooty of ASF in the Russian Federation, Ukraine was characterised by an increased number of outbreaks in domestic pigs. In the Russian Federation and in Ukraine, a large number of outbreaks were notified in the domestic pig sector: 215 and 62 outbreaks, respectively. About 80% of these outbreaks have been registered in small non-commercial pig farms where biosecurity is considered to be low. In August 2016, two outbreaks have been registered in regions of Ukraine, a further two outbreaks were registered in October 2016 in the Republic of Moldova bordering with Romania (WAHIS, 2016; not shown in Figure 1). As can be seen in Figure 1, ASF outbreaks in domestic pigs and in wild boar subpopulations can be linked or occur independently in time and space, pointing at existence of two parallel processes. 1.1 Background and Terms of Reference as provided by the requestor 1.1.1 Background ASF is a contagious infectious disease of domestic pigs and of the wild boar, usually fatal. No vaccine exists to combat this virus. It does not affect humans nor does it affect any animal species other than members of the Suidae family. From the beginning of 2014 up to 1/2/2016, Genotype II of ASF has been notified in Estonia, Latvia, Lithuania and Poland causing very serious concerns. The disease has also been reported in Russia, Belarus and Ukraine, which creates a constant risk for all the Member States (MS) bordering with these third countries. There is knowledge, legislation, technical and financial tools in the European Union (EU) to properly face ASF. The EU legislation primarily targets domestic pig and addresses, when needed, lays down specific aspects related to wild boar. The main pieces of the EU legislation relevant for ASF are: Council Directive 2002/60/EC3 of 27 June 2002 laying down specific provisions for the control of African swine fever and amending Directive 92/119/EEC as regards Teschen disease and African swine fever: it mainly covers prevention and control measures to be applied where ASF is suspected or confirmed either in holdings or in wild boars to control and eradicate the disease. Commission Implementing Decision 2014/709/EU4 of 9 October 2014 concerning animal health control measures relating to African swine fever in certain Member States and repealing Implementing Decision 2014/178/EU: it provides the animal health control measures relating to ASF in certain Member States by setting up a regionalisation mechanism in the EU. These measures involve mainly pigs, pig products and wild boar products. A map summarising the current regionalisation applied is available online.5 Council Directive No 82/894/EEC6 of 21 December 1982 on the notification of animal diseases within the Community which has the obligation for Member States to notify the Commission of the confirmation of any outbreak or infection of ASF in pigs or wild boar. The Commission is in need of an updated epidemiological analysis based on the data collected from the MS affected by ASF at the Eastern border of the EU. The use of the European Food Safety Authority (EFSA) Data Collection Framework (DCF) is encouraged given it promotes the harmonisation of data collection. Any data that is available from neighbouring third countries should be used as well. 1.1.2 Terms of Reference Analyse the epidemiological data on ASF from Estonia, Latvia, Lithuania, Poland and any other MS at the Eastern border of the EU that might be affected by ASF. Include an analysis of the temporal and spatial patterns of ASF in wild boar and domestic pigs. Include an analysis of the risk factors involved in the occurrence, spread and persistence of the ASF virus in the wild boar population and in the domestic/wildlife interface. Based on the findings from the point above, review the management options for wild boar identified in the EFSA scientific opinion of June 2015 and indicate whether the conclusions of the latest EFSA scientific opinion are still pertinent. 2 Data and methodologies This report analyses the temporal and spatial patterns of ASF in wild boar and domestic pigs, and analyses the risk factors involved in the occurrence of the ASF virus (ASFV) in the wild boar population, including the domestic/wildlife interface, based on the epidemiological data on ASF collected by Estonia, Latvia, Lithuania and Poland (Term of Reference 1). The currently available data does not allow estimating risk factors influencing the spread and persistence of ASFV. A review of the management options for wild boar identified in the EFSA scientific opinion of 2015 (Term of Reference 2) will be provided in a second scientific report in 2017. In order to allow for comprehensive epidemiological analysis and risk assessment, data provided by the MS in accordance with Directive 82/894/EEC to the ADNS was complemented with data from MS's laboratory testing for ASF, since both positive and negative findings are of interest for epidemiological explorations. To collect epidemiological data in a harmonised way EFSA, the Baltic States and Poland agreed on a common data model (database structure) which has been used for collecting laboratory data from the beginning of 2016.7 Details about the data model are provided in Appendix A. In June 2016, EFSA, in collaboration with its Latvian Focal Point, the Institute of Food Safety, Animal Health and Environment BIOR, organised a two-day workshop in Riga, Latvia, with 15 participants representing veterinary services, national laboratories and research institutions, to demonstrate what kind of epidemiological analyses can be carried out using the combined data collected by the MS. The needs for collecting additional data for more comprehensive analysis were also discussed. A specific EFSA DCF application is used to collect and validate data from laboratory testing for ASF from MS's LIMS. A summary of the data collected in the DCF is presented in Appendix B. Participants of the collaboration project (data providers and EFSA) share and use the data collated on the DCF on the basis of Data Sharing Agreements which lay down conditions of confidentiality and copyrights. 2.1 Data 2.1.1 Data for the spatio-temporal analysis 2.1.1.1 ASF notifications Data on ASFV detections in wild boar and domestic pigs reported between 24 January 2014 and 16 September 2016 were extracted from the ADNS. The number of outbreaks and cases are presented in Table 1. Table 1. Number of outbreaks in domestic pigs and cases in wild boar notified to the Animal Disease Notification System from 24 January 2014 until 16 September 2016 Country Outbreaks in domestic pigsa Cases in wild boar b Estonia 24 2,249 Latvia 44 2,068 Lithuania 37 534 Poland 20 188 a An outbreak of African swine fever in domestic pigs refers to one or more cases of ASF detected in a pig holding. b A case of African swine fever in wild boar refers to any wild boar or wild boar carcass in which clinical symptoms or post-mortem lesions attributed to ASF have been officially confirmed, or in which the presence of the disease has been officially confirmed as the result of a laboratory examination carried out in accordance with the diagnostic manual. The ADNS database contains the exact geographical location (longitude and latitude) and the number of cases for each outbreak. 2.1.1.2 Sample-based data The data on ASF tests from the LIMS of the national laboratories of the Baltic States and Poland have been collected in the DCF. The data model collects individual sample data using controlled terminology and coding systems, and includes such variables as the location of sampling (longitude and latitude or lowest available level of administrative unit), the description of animal sampled (hunted or found dead), its age and sex, including the rate of decomposition of carcass if the animal was found dead, the matrices sampled, and the method of analysis (virus or antibody detection). To maintain the quality of data, EFSA is providing summary statistics for each data set submitted, focusing on data that need corrections. The data reported to the DCF contains the information on samples tested for ASF in the period from January 2014 to June–August 2016. The LIMS data for 2016 has been collected using the agreed harmonised data model, while the data that were generated during the previous period (2014–2015), before the agreement of the harmonised data model, have been recoded as much as possible to fit the data model and allow for a joint analysis of the entire data set. As of December 2016, information on 232,722 tests for ASF, including 85,697 tests of domestic pigs samples and 147,025 tests of wild boar samples has been collated in the DCF (Figure 2). Figure 2Open in figure viewerPowerPoint Number of tests for ASF, from January 2014 to August 2016, submitted by the Member States to the DCF Samples were tested for ASF using polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), immunoblotting (IB) and immunoperoxidase test (IPT) methods. The geographical distribution of the samples sampled from wild boar and notifications, based on the data for the period of January 2014–August 2016 in Estonia, Latvia and Lithuania and for the period of January 2014–June 2016 in Poland collected in the DCF and on the notifications to the ADNS during this period, is shown in Figure 3. Figure 3Open in figure viewerPowerPoint Number of wild boar tested per 100 square km in 2014–2016 at NUTS 3 level. (A) hunted wild boar, (B) wild boar found dead Source: DCF. 2.1.2 Additional data used for the risk factor analysis In this report, available data on the following risk factors potentially involved in the occurrence of the ASF virus in the wild boar population and at the domestic/wildlife interface were used for the analyses. 2.1.2.1 Environmental and demographic data Land cover Data on the land cover of the Baltic states and Poland were obtained from the Corine Land Cover (CLC) map 2006 (version CLC2006; European Environment Agency, Copenhagen, Denmark) with a spatial resolution of 100 × 100 m, (EEA, 1984) and converted from the raster into a percentage of wetlands, water bodies, forests, permanent crops of the total area of the administrative units, using the ArcGIS software (Spatial analyst module, Zonal statistic tool). The data on the human population for 2015 at district (LAU 1) level have been extracted from official national statistics institutions' web sites: the Central Statistical Office of Poland (available on: http://stat.gov.pl, http://www.coloss.org/beebook, last accessed 1 August 2016), Statistics Lithuania (available on: http://www.stat.gov.lt, last accessed 1 August 2016), the Central Statistical Bureau of Latvia (available on: http://www.csb.gov.lv, last accessed 1 August 2016) and Statistics Estonia (http://www.stat.ee, last accessed 1 August 2016). Density of settlements, national and regional roads The locations of settlements and national and regional roads were obtained from the website of the GIS-LAB Project (available on: http://gis-lab.info/qa/osmshp.html, last accessed 1 August 2016) for Estonia, Latvia and Lithuania and from The National Veterinary Research Institute of Poland, as shape files. They were combined with the shape files of administrative units using ArcGIS. For the analyses, the number of settlements and number of roads within each administrative unit's polygon were used. 2.1.2.2 Susceptible population data Domestic pig population distribution Data on the domestic pig population and distribution were provided by the MS. Table 2 provides a summary of the type of data made available to EFSA for the assessment. Data on the domestic pig population with appropriate spatial resolution and details were not available for Poland. The number of small pig farms (< 10 heads) have been used as a covariate which could characterise farms with a low level of biosecurity. Table 2. Data provided by the relevant member states on pig population and distribution MS DATA Admin resolution YEARS Estonia Pigs population size at herd level Exact location of holdings 2014–2016 Latvia Pigs population size Number of holdings Number of small holdings Number of sows LAU2 2014–2016 Lithuania Pigs population size LAU 1 2014–2016 Number of holdings LAU 1 2016 Poland Number of pigs NUTS 3 2015 Wild boar population distribution The size of wild boar populations (based on national hunters organisations' estimates of population size in the spring of 2014, 2015 and 2016) and the wild boar density (individuals per 1,000 ha or 10 km2) were provided by national wildlife institutions of Estonia, Latvia and Poland at ‘hunting ground’ level (Appendix D), and at NUTS3 level for Lithuania. The data provided by Estonia include also yearly numbers of hunted wild boar, wild boar road kills and wild boar found dead. All data were recoded to administrative unit level using generation of random points and spatial aggregation using ArcGIS. 2.1.2.3 Aggregation of data For each administrative unit, the areal percentage of the different types of land cover, human population, wild boar and domestic pig population were considered as potential influencing covariates in the risk factor analysis. All covariates were aggregated spatially on the basis of the shape file of the administrative units at three different levels: NUTS 3, LAU 1 and LAU 2. 2.1.3 Summary of data used in the risk factor analysis Information regarding available potential risk factors were transformed in order to use them in the risk factor analysis considering a common scale. The list of available risk factors provided by MS involved in the assessment is summarised in Table 3. Table 3. Available risk factors provided by Member States involved in the assessment Potential risk factor Abbreviation Latvia Estonia Lithuania Poland Human population proportion HPPrp X X X O Proportion of the number of roads RdsPrp X X X X Proportion of number of settlements StlmPrp X X X X Forest area proportion FrstPrp X X X X Water bodies area proportion WtrbdsPrp X X X X Percentage of area of wetlands PrcnWtlnd X X X X Percentage of are of inland wetlands PrcInWtln X X X X Wild boar density (ind./10 km2) WBDens X X X X Proportion of number of pig farms PrpNmPgFrms X X O O Proportion of number of pigs PrpNmPg X X X O Proportion of small pig farms (less than 10 animals) PrpPgFms1_10 X X O O Proportion of number of pigs in small pig farms (less than 10 animals) PrpNmPgs_1_10 O X O O X: available; O: not available. The information provided were transformed to relative proportions considering the spatial resolutions used in the risk factor analysis for each MS, using the maximum value reported for all years as the reference point, and considering the ratio of each region value with respect to the maximum value reported. Relative proportions in a given region were calculated for: Geographical Factors – Number of roads (number of asphalted roads) – Forest area (area of broad-leaved forest, coniferous and mixed forest) – Number of settlements (number of settlements (dots) within administrative unit) – Water bodies (area of water courses, water bodies, coastal lagoons and estuaries) Population Characteristics – Human Population (total number of people) – Number of pigs (total number of pigs) – Number of pig farms (total number of pig holdings) – Number of small pig farms (number farms with less than 10 animals) – Number of pigs in small farms (total number of pigs kept in small pig farms). Also, the proportion of area of maritime wetlands (salt marshes, salines and intertidal flats) and inland wetlands (inland marshes and peat bogs) were calculated, considering the area of the region as the denominator and later convert it to percentages. Wild boar density was calculated using the number of animals divided by the area of the region divided by 10, to express it as a function of 10 km2 (or 10,000 ha). 2.2 Methodologies Data from the DCF were extracted and collated using analytics software SAS Enterprise Guide 5.1 (http://www.sas.com/) before carrying out the analyses described in detail below. 2.2.1 Spatio-temporal analysis Data processing and visualisation of spatio-temporal spread of the disease in the wild boar populations were performed using geographic information system software ArcGIS 10.2 (http://www.esri.com/). An analysis of clusters was carried out to visualise local spread of the virus. A cluster is defined as a group of ASF notifications in wild boar which are temporally and spatially linked. For the explicit spatial clusters established in the previous period (January 2014–April 2015), that have been described in the EFSA scientific opinion on ASF (EFSA AHAW Panel, 2015), as well as in the clusters formed in the subsequent period (up to September 2016), the mean centre and standard distance were defined by corresponding tools of the Spatial analyst module of Arc Map 10.2. The mean centre identifies the geographic centre (or the centre of concentration) for a set of features (longitude and latitude values). The standard distance measures the degree to which features are concentrated or dispersed around the geographic mean centre (1 standard deviation). These two parameters were defined by corresponding tolls of the Spatial Analyst module of Arc Map 10.2. Statistical models that deal with data that is collected across space (i.e. different regions) and possibly over time (i.e. different years) have been used. The analysis of such data types takes into account the spatial and/or temporal dependence of the observations. The linear component of the spatio-temporal model for the binary data for the presence of ASF (ASF status, time and location) can be written including a random effect accommodating temporal dependence, and another one to account for spatial dependence, as well as the p
Referência(s)