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Crimean-Congo hemorrhagic fever (CCHF) is an emerging tickborne disease and a World Health Organization priority. Although humans are accidental hosts, infection can lead to hemorrhagic fever with a high fatality rate. Domestic animals play a critical role in disease transmission, but infected animals do not show clinical signs and viremia is short; thus, CCHF virus (CCHFV) infections can remain unobserved. During 2017-2019, we conducted 2 sequential observational studies followed by a multisite randomized controlled trial to determine spatial-temporal patterns and quantify drivers for CCHFV exposure in a natural host (sheep) in a CCHF-endemic area of Bulgaria. We found high-risk areas embedded in endemic regions. Animal characteristics were not correlated with seropositivity; however, a seasonality effect was observed, suggesting sampling time was a potential confounder. Force of infection varied across farms and over time. CCHFV transmission heterogeneity among farms is driven by preventive measures used to reduce exposure to ticks.

Crimean-Congo haemorrhagic fever virus (CCHFV) and Nairobi sheep disease virus (NSDV) are orthonairoviruses of concern, able to cause haemorragic fever disease in humans and sheep, respectively. CCHFV and NSDV cocirculating in small ruminant populations across South Asia and East Africa. Cross-reactivity to viruses of the Orthonairovirus genus can potentially interfere with serological assays when employed for serosurveillance in regions where two or more genus members overlap in their distribution. In this study, sheep sera sampled from a region of confirmed CCHFV circulation and NSDV absence were utilized, thereby eliminating the possibility of co-exposure. Field sera were tested against in-house anti-NSDV ELISAs specific to the nucleoprotein (NSDV NP) and glycoprotein C (NSDV Gc) antigens as well as an in-house NSDV 80% plaque reduction neutralization test (PRNT ). We assessed whether there is a correlation between CCHFV- and NSDV-specific ELISAs. Furthermore, epitopes-derived from CCHFV antigens for sheep antibody that were available from the literature were analyzed. When comparing NSDV antigen-specific antibody responses against previously tested CCHFV antigen-specific antibody responses, a strong positive correlation was observed between the Gc-specific responses, while a weak positive correlation was observed between the NP-specific responses. Consequently, NP-specific ELISAs have a higher assay specificity compared to Gc-specific ELISAs, making them more suitable for serosurveillance in regions where multiple orthonairoviruses co-circulate. Crucially, only one seropositive sample to NSDV Gc-specific out of a set of 224 (0.4%) showed a neutralizing capacity at the lowest serum dilution (1:8), suggesting these field sera have not been exposed to NSDV. Based on an analysis of known epitopes in NP targeted by antibodies in sheep serum, we propose that NP is less cross-reactive because dominant epitopes are highly dissimilar between CCHFV and NSDV. Gc exhibited a strong cross-reaction while the NP was weakly cross-reactive due to dominant epitopes being highly dissimilar between CCHFV and NSDV. Our in-house PRNT80 assay can could be used as a confirmatory test in regions where CCHFV and NSDV circulate.

African swine fever (ASF) is one of the most threatening diseases for the pig farming sector worldwide. As an effective vaccine is lacking, strict application of control measures is the only way to fight the disease in both industrial farms and backyard holdings. With generally low biosecurity standards, the latter are at particular risk for disease introduction and offer challenging conditions for disease control. In the following case report, we describe the overall course of an ASF outbreak in a Bulgarian backyard farm and the implemented control measures. Farm facilities and available data have been investigated to estimate the possible source, spread and time point of virus introduction. Contact with contaminated fomites entering the stable via human activities was regarded to be the most likely introduction route. The slow disease spread within the farm contributes to the hypothesis of a moderate contagiosity. As no further ASF outbreaks have been detected in domestic pig farms in the region, it could be demonstrated that successful disease control in small-scale farms can be reached. Thus, the report contributes to a better understanding of ASF in the backyard sector.

Improvements to sequencing protocols and the development of computational phylogenetics have opened up opportunities to study the rapid evolution of RNA viruses in real time. In practical terms, these results can be combined with field data in order to reconstruct spatiotemporal scenarios that describe the origin and transmission pathways of viruses during an epidemic. In the case of notifiable diseases, such as foot-and-mouth disease (FMD), these analyses provide important insights into the epidemiology of field outbreaks that can support disease control programmes. This study reconstructs the origin and transmission history of the FMD outbreaks which occurred during 2011 in Burgas Province, Bulgaria, a country that had been previously FMD-free-without-vaccination since 1996. Nineteen full genome sequences (FGS) of FMD virus (FMDV) were generated and analysed, including eight representative viruses from all of the virus-positive outbreaks of the disease in the country and 11 closely-related contemporary viruses from countries in the region where FMD is endemic (Turkey and Israel). All Bulgarian sequences shared a single putative common ancestor which was closely related to the index case identified in wild boar. The closest relative from outside of Bulgaria was a FMDV collected during 2010 in Bursa (Anatolia, Turkey). Within Bulgaria, two discrete genetic clusters were detected that corresponded to two episodes of outbreaks that occurred during January and March-April 2011. The number of nucleotide substitutions that were present between, and within, these separate clusters provided evidence that undetected FMDV infection had occurred. These conclusions are supported by laboratory data that subsequently identified three additional FMDV-infected livestock premises by serosurveillance, as well as a number of antibody positive wild boar on both sides of the border with Turkish Thrace. This study highlights how FGS analysis can be used as an effective on-the-spot tool to support and help direct epidemiological investigations of field outbreaks.

Crimean-Congo hemorrhagic fever (CCHF) is a priority emerging disease. CCHF, caused by the CCHF virus (CCHFV), can lead to hemorrhagic fever in humans with severe cases often having fatal outcomes. CCHFV is maintained within a tick-vertebrate-tick cycle, which includes domestic animals. Domestic animals infected with CCHFV do not show clinical signs of the disease and the presence of antibodies in the serum can provide evidence of their exposure to the virus. Current serological tests are specific to either one CCHFV antigen or the whole virus antigen. Here, we present the development of two in-house ELISAs for the detection of serum IgG that is specific for two different CCHFV antigens: glycoprotein Gc (CCHFV Gc) and nucleoprotein (CCHFV NP). We demonstrate that these two assays were able to detect anti-CCHFV Gc-specific and anti-CCHFV NP-specific IgG in sheep from endemic CCHFV areas with high specificity, providing new insight into the heterogeneity of the immune response induced by natural infection with CCHFV in domestic animals.

The spatial pattern and epidemiology of backyard pig farming and other low bio-security pig production systems and their role in the occurrence of classical swine fever (CSF) is described and evaluated. A spatial Bayesian model was used to explore the risk factors, including human demographics, socioeconomic and environmental factors. The analyses were performed for Bulgaria, which has a large number of backyard farms (96% of all pig farms in the country are classified as backyard farms), and it is one of the countries for which both backyard pig and farm counts were available. Results reveal that the high-risk areas are typically concentrated in areas with small family farms, high numbers of outgoing pig shipments and low levels of personal consumption (i.e. economically deprived areas). Identification of risk factors and high-risk areas for CSF will allow to targeting risk-based surveillance strategies leading to prevention, control and, ultimately, elimination of the disease in Bulgaria and other countries with similar socio-epidemiological conditions.

Understanding the complexity of live pig trade organization is a key factor to predict and control major infectious diseases, such as classical swine fever (CSF) or African swine fever (ASF). Whereas the organization of pig trade has been described in several European countries with indoor commercial production systems, little information is available on this organization in other systems, such as outdoor or small-scale systems. The objective of this study was to describe and compare the spatial and functional organization of live pig trade in different European countries and different production systems. Data on premise characteristics and pig movements between premises were collected during 2011 from Bulgaria, France, Italy, and Spain, which swine industry is representative of most of the production systems in Europe (i.e., commercial vs. small-scale and outdoor vs. indoor). Trade communities were identified in each country using the Walktrap algorithm. Several descriptive and network metrics were generated at country and community levels. Pig trade organization showed heterogeneous spatial and functional organization. Trade communities mostly composed of indoor commercial premises were identified in western France, northern Italy, northern Spain, and north-western Bulgaria. They covered large distances, overlapped in space, demonstrated both scale-free and small-world properties, with a role of trade operators and multipliers as key premises. Trade communities involving outdoor commercial premises were identified in western Spain, south-western and central France. They were more spatially clustered, demonstrated scale-free properties, with multipliers as key premises. Small-scale communities involved the majority of premises in Bulgaria and in central and Southern Italy. They were spatially clustered and had scale-free properties, with key premises usually being commercial production premises. These results indicate that a disease might spread very differently according to the production system and that key premises could be targeted to more cost-effectively control diseases. This study provides useful epidemiological information and parameters that could be used to design risk-based surveillance strategies or to more accurately model the risk of introduction or spread of devastating swine diseases, such as ASF, CSF, or foot-and-mouth disease.

In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.