Modeling the live-pig trade network in Georgia: Implications for disease prevention and control.

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant

Spatial analysis of lumpy skin disease in Eurasia - Predicting areas at risk for further spread within the region

Data from affected lumpy skin disease (LSD) locations between July 2012 and September 2018 in the Balkans, Caucasus, and Middle East were retrieved from FAO's Global Animal Disease Information System (EMPRES-i) from the European Commission's Animal Disease Notification System (ADNS) and completed with data from the official veterinary services of some countries. During this period, a total of 7,593 locations from 22 countries were affected. Within this period, over 46,000 cattle were clinically affected by LSD, 3,700 animals died and 17,500 were slaughtered due to culling policies to stop the spread of the disease. Most outbreaks occurred in 2016, between the months of May and November. The affected region was divided into a grid of 10 × 10 km cells and we fit a spatial regression model to analyse the association between the reported LSD outbreaks and climatic variables, land cover, and cattle density. The results showed big differences in the odds of being LSD positive due to the type of land cover: the odds of a cell being LSD positive was increased in areas mostly covered with croplands, grassland, or shrubland. The odds was also increased for higher cattle density, as well as areas with higher annual mean temperature and higher temperature diurnal range. The resulting model was utilized to predict the LSD risk in neighbouring unaffected areas in Europe, the Caucasus, and Central Asia, identifying several areas with high risk of spread. Results from this study provide useful information for the design of surveillance and awareness systems, and preventive measures, e.g., vaccination programmes.info:eu-repo/semantics/acceptedVersio

Cost-effective tools and strategies for the early detection of avian influenza in poultry

La Influenza Aviar (IA) es una enfermedad altamente contagiosa que afecta a los sistemas respiratorio, digestivo y nervioso de aves domésticas y silvestres. La IA se ha convertido en un importante problema de salud veterinaria y pública (debido a su potencial zoonótico). La IA tiene una distribución mundial. Existen numerosas cepas de virus y las aves acuáticas son los reservorios naturales de todos ellos. La IA puede transmitirse de las aves silvestres a las aves de corral, pero después se perpetúa en las aves de corral debido a factores humanos, es decir, el contacto directo con aves de corral infectadas, o por medio de fomites (personas, vehículos, etc.). Los mercados de aves vivas y las poblaciones de aves de traspatio no reguladas juegan un papel crítico en la propagación de la IA. La presentación de la IA puede conducir a una variedad de presentaciones clínicas, dependiendo principalmente de la cepa y las especies afectadas. La influenza aviar de alta altamente patógena (IAAP) pueden alcanzar mortalidades de hasta el 100% en las aves domésticas terrestres (pollos y pavos), pero a menudo no producen ningún signo clínico en aves acuáticas domésticas. En cambio, la influenza aviar de baja patogenicidad (IABP) se presenta a menudo como infecciones inaparentes o enfermedad respiratoria leve, por lo que a menudo pasa desapercibida. La IAAP tiene que ser notificada y controlada de acuerdo con todas las normas nacionales e internacionales, pero la situación no siempre es tan clara para la IABP. La epidemia mundial de H5N1 IAAP ha atraído una gran atención por su magnitud sin precedentes históricos. Dado que no existe un tratamiento eficaz para la IA, impedir su entrada en las poblaciones de aves de corral, y controlarla tan pronto como se detecta son las mejores formas de minimizar el impacto de la enfermedad. Tanto la prevención como el control dependen en gran medida de que el sistema de vigilancia epidemiológica sea eficaz, lo que permite la detección temprana y proporciona información sobre el estado de la enfermedad y la eficacia de las medidas vigentes. La sensibilización y la formación de todos los involucrados es un enfoque transversal con impacto directo en la ejecución de los tres componentes, es decir, la prevención, el control y las actividades de vigilancia epidemiológica. Estrategias de vigilancia para la IA varían de país a país y con el tiempo, dependiendo del estado de la infección y el riesgo del país, y si se trata de la IAAP, IABP notificable o IABP. En cualquier caso, las estrategias de vigilancia deben incluir actividades para monitorear las poblaciones de aves silvestres y de corral utilizando una combinación de métodos pasivos y activos. La vigilancia epidemiológica, en particular los métodos activos, puede ser muy cara. Cuando se trabaja con presupuestos limitados, a menudo insuficientes, la rentabilidad se convierte en el criterio de mayor importancia en el diseño de un programa de vigilancia. El uso de evaluaciones de riesgo regulares ayudará a identificar en qué localidades, poblaciones y especies enfocar la vigilancia epidemiológica. Los costos también pueden reducirse si las tareas de vigilancia se combinan con la aplicación de otras actividades sobre el terreno, como las evaluaciones y mejoras de bioseguridad. De todos los tipos de vigilancia epidemiológica, la pasiva es la más rentable si se aplica de manera efectiva. Sin embargo, para la IABP, donde los signos clínicos pueden ser inaparentes o muy leves, la vigilancia pasiva convencional no será eficiente. En estos casos, la vigilancia sindrómica, un enfoque muy novedoso de vigilancia pasiva, presenta una alternativa muy prometedora.Avian Influenza (AI), a highly contagious disease affecting the respiratory, digestive and nervous systems of domestic and wild bird species, has become a major veterinary and public health concern (due to its potential to infect humans). AI occurs worldwide. There are numerous virus strains and aquatic birds are the natural virus reservoirs of all of them. AI can be transmitted from wild birds to poultry, but afterwards it is often perpetuated in poultry by transmission via human-driven factors, i.e. direct contact with infected poultry, or through fomites, i.e. people, vehicles, etc. Live bird markets and unregulated backyard bird populations with low biosecurity play critical roles in AI spread. In poultry, AI can lead to a variety of clinical presentations, depending mostly on the strain and species affected. Highly pathogenic avian influenza (HPAI) mortalities can reach 100% in terrestrial poultry (e.g. chickens and turkeys), but often lead to no signs in domestic waterfowl. Instead, low pathogenic avian influenza viruses (LPAIVs) may result in inapparent infections or mild respiratory disease that often go unnoticed. HPAI have to be reported and controlled according to all national and international regulations, but the situation is not always so clear for LPAI. The current H5N1 HPAI panzootic has attracted great attention due to its historically unprecedented magnitude. Since there is no effective treatment for AI, preventing it the entry into poultry populations, and controlling it as soon as it is detected are the best ways to minimise the impact of the disease. Both prevention and control largely rely on and effective surveillance system to provide with early detection and to inform on the disease status and the effectiveness of measures in place. Awareness raising and training of all relevant stakeholders is a cross-cutting approach with direct impact in the implementation of all three components, i.e. prevention, control and surveillance activities. Surveillance strategies for AI vary from country to country and over time, depending on the infection and risk status of the country, and whether dealing with HPAI, reportable LPAI or LPAI. In any case, surveillance strategies should include activities to monitor wild bird populations and poultry using a combination of passive and active approaches. Surveillance, particularly active approaches, can be very expensive. When dealing with limited, often insufficient budgets, cost-effectiveness becomes the top criterium in the design a surveillance program. The use of regular risk assessments will help to prioritize the sites, populations and species to target. Costs can also be reduced if surveillance tasks are combined with the implementation of other field activities, e.g. biosecurity assessments and improvement. Of all surveillance types, passive surveillance is the most cost-effective if implemented effectively. However, for LPAI, where clinical signs may be inapparent or very mild, conventional passive surveillance will not be efficient, and syndromic surveillance, a very novel approach of passive surveillance, presents a promising alternative

Targeting the search of African swine fever-infected wild boar carcasses : A tool for early detection

Acord transformatiu CRUE-CSICThis study analyses the temporal and spatial distribution of found dead African swine fever (ASF)-positive wild boar carcasses from 2017 to January 2021 in affected European countries: Bulgaria, Estonia, Germany, Hungary, Latvia, Lithuania, Romania, Poland, Serbia and Slovakia. During this period, a total of 21,785 cases were confirmed in 19,071 unique locations. The temporal analysis of aggregated cases per month evidenced that most countries located in southern latitudes showed a higher number of cases between January and April, whereas in northern latitudes there was no clear temporal pattern. The space-time K-function evidenced a space-time clustering in the ASF-positive wild boar carcasses, which was most prominent within distances of 2 km and within 1 week. A Bayesian hierarchical spatial model was calibrated to evaluate the association between the probability of finding ASF-positive wild boar carcasses and landscape factors (i.e. the presence of a path and paved road), land use and wild boar abundance. Results showed the highest likelihood of finding ASF-positive wild boar carcasses in areas of transition between woodland and shrub, green urban areas and mixed forests. The presence of a path and a higher abundance of wild boar also increased slightly the odds of finding an ASF-positive dead wild boar. In summary, this paper aims to provide recommendations to design a search strategy to find ASF-infected wild boar carcasses, which is a crucial activity in the management of the disease, not just for surveillance purposes (i.e. the early detection of an introduction and the regular monitoring to understand the epidemiology and dynamics), but also for control, namely the disposal of infected carcasses as a virus source

Global trends in infectious diseases at the wildlife–livestock interface

The role and significance of wildlife–livestock interfaces in disease ecology has largely been neglected, despite recent interest in animals as origins of emerging diseases in humans. Scoping review methods were applied to objectively assess the relative interest by the scientific community in infectious diseases at interfaces between wildlife and livestock, to characterize animal species and regions involved, as well as to identify trends over time. An extensive literature search combining wildlife, livestock, disease, and geographical search terms yielded 78,861 publications, of which 15,998 were included in the analysis. Publications dated from 1912 to 2013 and showed a continuous increasing trend, including a shift from parasitic to viral diseases over time. In particular there was a significant increase in publications on the artiodactyls–cattle and bird–poultry interface after 2002 and 2003, respectively. These trends could be traced to key disease events that stimulated public interest and research funding. Among the top 10 diseases identified by this review, the majority were zoonoses. Prominent wildlife–livestock interfaces resulted largely from interaction between phylogenetically closely related and/or sympatric species. The bird–poultry interface was the most frequently cited wildlife–livestock interface worldwide with other interfaces reflecting regional circumstances. This review provides the most comprehensive overview of research on infectious diseases at the wildlife–livestock interface to date

Examination of critical factors influencing ruminant disease dynamics in the Black Sea Basin

IntroductionRuminant production in the Black Sea basin (BSB) is critical for national economies and the subsistence of rural populations. Yet, zoonoses and transboundary animal diseases (TADs) are limiting and threatening the sector. To gain a more comprehensive understanding, this study characterizes key aspects of the ruminant sector in nine countries of the BSB, including Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Moldova, Romania, Türkiye, and Ukraine.MethodsWe selected six priority ruminant diseases (anthrax, brucellosis, Crimean Congo haemorrhagic fever (CCHF), foot-and-mouth disease (FMD), lumpy skin disease (LSD), and peste des petits ruminants (PPR)) that are present or threaten to emerge in the region. Standardized questionnaires were completed by a network of focal points and supplemented with external sources. We examined country and ruminant-specific data such as demographics, economic importance, and value chains in each country. For disease-specific data, we analysed the sanitary status, management strategies, and temporal trends of the selected diseases.Results and discussionThe shift from a centrally planned to a market economy, following the collapse of the Soviet Union, restructured the ruminant sector. This sector played a critical role in rural livelihoods within the BSB. Yet, it faced significant challenges such as the low sustainability of pastoralism, technological limitations, and unregistered farms. Additionally, ruminant health was hindered by informal animal trade as a result of economic factors, insufficient support for the development of formal trade, and socio-cultural drivers. In the Caucasus and Türkiye, where diseases were present, improvements to ruminant health were driven by access to trading opportunities. Conversely, European countries, mostly disease-free, prioritized preventing disease incursion to avoid a high economic burden. While international initiatives for disease management are underway in the BSB, there is still a need for more effective local resource allocation and international partnerships to strengthen veterinary health capacity, protect animal health and improve ruminant production

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control.

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control.

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant

Epidemiology of African swine fever in Africa today : Sylvatic cycle versus socio‐economic imperatives

International audienceAfrican swine fever (ASF) is believed to have evolved in eastern and southern Africa in a sylvatic cycle between common warthogs (Phacochoerus africanus) and argasid ticks of the Ornithodoros moubata complex that live in their burrows. The involvement of warthogs and possibly other wild suids in the maintenance of ASF virus means that the infection cannot be eradicated from Africa, but only prevented and controlled in domestic pig populations. Historically, outbreaks of ASF in domestic pigs in Africa were almost invariably linked to the presence of warthogs, but subsequent investigations of the disease in pigs revealed the presence of another cycle involving domestic pigs and ticks, with a third cycle becoming apparent when the disease expanded into West Africa where the sylvatic cycle is not present. The increase in ASF outbreaks that has accompanied the exponential growth of the African pig population over the last three decades has heralded a shift in the epidemiology of ASF in Africa, and the growing importance of the pig husbandry and trade in the maintenance and spread of ASF. This review, which focuses on the ASF situation between 1989 and 2017, suggests a minor role for wild suids compared with the domestic cycle, driven by socio‐economic factors that determine the ability of producers to implement the control measures needed for better management of ASF in Africa

Frequency distribution of the studied goodness of fit diagnostic parameters of the m2 (final) exponential random graph model of the swine trade industry in Georgia, during a twelve-month period.

<p>Black lines represent the observed data. Boxplots cover the values of 100 randomly-simulated networks that conform to the model; whiskers represent the 95% CI.</p