Identifying sites where wild boars can consume anthropogenic food waste with implications for African swine fever.

Wild boar population dynamics promote the increase in numbers and distribution of the species in Eurasia, leading to a rise in the interaction with human activities, as well as generating problems with the management of certain infectious diseases, most notably African swine fever (ASF). ASF virus possesses high stability in several contaminated pork and pork products that can be a source of indirect transmission to susceptible hosts habituated to anthropogenic food waste. This transmission route is a concerning threat for the dispersion of the disease, primarily into unaffected areas given the worldwide widespread distribution of the disease and the increase of wild boar contact with humans. Thus, in this study, a straightforward tool to assess the relative risk of wild boar natural populations potentially consuming food waste is presented using synthetic data. Three risk groups were defined related to urban areas, travel, and leisure. The surrounding quality of habitat of wild boar was used to obtain the relative risk of wild boar potentially consuming anthropogenic food waste. To assign the relative risk to the corresponding risk unit, we also included the population for the urban areas group, and traffic volume for the travel risk group. The leisure group had higher scaled risk scores, followed by the urban areas group. Higher risk was found in the edges of the study area where more natural landscapes are found. The implications of this risk are discussed focusing on the context of ASF transmission. The outputs can help prioritize decision-making in terms of the improvement of preventive measures against the habituation of wild boar to anthropogenic food waste and ASFV introduction in a given study area.

Geospatial analysis for strategic wildlife disease surveillance: African swine fever in South Korea (2019-2021).

Since the confirmation of African swine fever (ASF) in South Korea in 2019, its spread, predominantly in wild boars, has been a significant concern. A key factor in this situation is the lack of identification of risk factors by surveillance bias. The unique orography, characterized by high mountains, complicates search efforts, leading to overlooked or delayed case detection and posing risks to the swine industry. Additionally, shared rivers with neighboring country present a continual threat of virus entry. This study employs geospatial analysis and statistical methods to 1) identify areas at high risk of ASF occurrence but possibly under-surveilled, and 2) indicate strategic surveillance points for monitoring the risk of ASF virus entry through water bodies and basin influences. Pearson's rho test indicated that elevation (rho = -0.908, p-value < 0.001) and distance from roads (rho = -0.979, p-value < 0.001) may have a significant impact on limiting surveillance activities. A map of potential under-surveilled areas was created considering these results and was validated by a chi-square goodness-of-fit test (X-square = 208.03, df = 1, p-value < 0.001). The strong negative correlation (rho = -0.997, p-value <0.001) between ASF-positive wild boars and distance from water sources emphasizes that areas surrounding rivers are one of the priority areas for monitoring. The subsequent hydrological analyses provided important points for monitoring the risk of virus entry via water from the neighboring country. This research aims to facilitate early detection and prevent further spread of ASF.

Application of machine learning with large-scale data for an effective vaccination against classical swine fever for wild boar in Japan.

Classical swine fever has been spreading across the country since its re-emergence in Japan in 2018. Gifu Prefecture has been working diligently to control the disease through the oral vaccine dissemination targeting wild boars. Although vaccines were sprayed at 14,000 locations between 2019 and 2020, vaccine ingestion by wild boars was only confirmed at 30% of the locations. Here, we predicted the vaccine ingestion rate at each point by Random Forest modeling based on vaccine dissemination data and created prediction surfaces for the probability of vaccine ingestion by wild boar using spatial interpolation techniques. Consequently, the distance from the vaccination point to the water source was the most important variable, followed by elevation, season, road density, and slope. The area under the curve, model accuracy, sensitivity, and specificity for model evaluation were 0.760, 0.678, 0.661, and 0.685, respectively. Areas with high probability of wild boar vaccination were predicted in northern, eastern, and western part of Gifu. Leave-One-Out Cross Validation results showed that Kriging approach was more accurate than the Inverse distance weighting method. We emphasize that effective vaccination strategies based on epidemiological data are essential for disease control and that our proposed tool is also applicable for other wildlife diseases.

What can we learn from the five-year African swine fever epidemic in Asia?

Today's global swine industry is exposed to the unprecedented threat of African swine fever (ASF). Asia, the site of the most recent epidemics, could serve as a huge viral reservoir for the rest of the world given the severity of the damage, the huge swine industry, and the high volume of trade with other countries around the world. As the majority of ASF notifications in Asia today originate from pig farms, the movement of live pigs and associated pork products are considered critical control points for disease management. Particularly, small-scale or backyard farms with low biosecurity levels are considered major risk factors. Meanwhile, wild boars account for most notified cases in some countries and regions, which makes the epidemiological scenario different from that in other Asian countries. As such, the current epidemic situation and higher risk factors differ widely between these countries. A variety of studies on ASF control have been conducted and many valuable insights have been obtained in Asia; nevertheless, the overall picture of the epidemic is still unclear. The purpose of this review is to provide an accurate picture of the epidemic situation across Asia, focusing on each subregion to comprehensively explain the disease outbreak. The knowledge gained from the ASF epidemics experienced in Asia over the past 5 years would be useful for disease control in areas that are already infected, such as Europe, as well as for non-affected areas to address preventive measures. To this end, the review includes two aspects: a descriptive analytical review based on publicly available databases showing overall epidemic trends, and an individualized review at the subregional level based on the available literature.

Quantitative risk assessment of African swine fever introduction into Spain by legal import of swine products.

African swine fever (ASF) is currently threatening the global swine industry. Its unstoppable global spread poses a serious risk to Spain, one of the world's leading producers. Over the past years, there has been an increased global burden of ASF not only in swine but also swine products. Unfortunately, many pigs are not diagnosed before slaughter and their products are used for human consumption. These ASF-contaminated products are only a source for new ASF outbreaks when they are consumed by domestic pigs or wild boar, which may happen either by swill feeding or landfill access. This study presents a quantitative stochastic risk assessment model for the introduction of ASF into Spain via the legal import of swine products, specifically pork and pork products. Entry assessment, exposure assessment, consequence assessment and risk estimation were carried out. The results suggest an annual probability of ASF introduction into Spain of 1.74 × 10-4, the highest risk being represented by Hungary, Portugal, and Poland. Monthly risk distribution is homogeneously distributed throughout the year. Illegal trade and pork product movement for own consumption (e.g., air and ship passenger luggage) have not been taken into account due to the lack of available, accredited data sources. This limitation may have influenced the model's outcomes and, the risk of introduction might be higher than that estimated. Nevertheless, the results presented herein would contribute to allocating resources to areas at higher risk, improving prevention and control strategies and, ultimately, would help reduce the risk of ASF introduction into Spain.

A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms.

Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming.

Spatio-Temporal Epidemiology of the Spread of African Swine Fever in Wild Boar and the Role of Environmental Factors in South Korea.

Since the first confirmation of African swine fever (ASF) in domestic pig farms in South Korea in September 2019, ASF continues to expand and most notifications have been reported in wild boar populations. In this study, we first performed a spatio-temporal cluster analysis to understand ASF spread in wild boar. Secondly, generalized linear logistic regression (GLLR) model analysis was performed to identify environmental factors contributing to cluster formation. In the meantime, the basic reproduction number (R0) for each cluster was estimated to understand the growth of the epidemic. The cluster analysis resulted in the detection of 17 spatio-temporal clusters. The GLLR model analysis identified factors influencing cluster formation and indicated the possibility of estimating ASF epidemic areas based on environmental conditions. In a scenario only considering direct transmission among wild boar, R0 ranged from 1.01 to 1.5 with an average of 1.10, while, in another scenario including indirect transmission via an infected carcass, R0 ranged from 1.03 to 4.38 with an average of 1.56. We identified factors influencing ASF expansion based on spatio-temporal clusters. The results obtained would be useful for selecting priority areas for ASF control and would greatly assist in identifying efficient vaccination areas in the future.

The Role of the Wild Boar Spreading African Swine Fever Virus in Asia: Another Underestimated Problem.

African swine fever (ASF) is a highly lethal infectious disease in naive populations of domestic pigs and wild boar. In Asia, from the first outbreak in August 2018 until the end of November 2021, ASF has been reported in 16 Asian countries. The ASF virus (ASFV) circulation in domestic pigs is considered the main problem in Asia. On the other hand, there are very few reports of ASF in wild boar in this region. However, considering the high wild boar density within the same area of smallholder domestic pig farms in Asia, the occurrence of ASFV infection in wild boar may be underestimated. The role of the wild boar in other ASF epidemiological scenarios, such as Europe, is a key for the maintenance and transmission of the disease. Hence, we performed a preliminary study estimating the extent of ASFV infection in the Asian wild boar population. The potential risk area of ASF-infected wild boar was calculated based on the habitat suitability for wild boar, the kernel density of ASF notification in smallholder farms and wild boar, and the ASFV transmission rate of wild boar. As a result of the analysis, high-, medium-, and low-risk areas were identified throughout Southeast and East Asia. The highest risk area was detected in China, followed by Myanmar, Far East Russia, Thailand, Vietnam, Laos, Cambodia, and the Philippines. Additionally, another risk area was detected from northeastern China to the Korean Peninsula, including Far East Russia. This study shows hot spots where a high risk of infection in wild boar is most likely to occur, helping to control ASF.

Environment, vector, or host? Using machine learning to untangle the mechanisms driving arbovirus outbreaks.

Climatic, landscape, and host features are critical components in shaping outbreaks of vector-borne diseases. However, the relationship between the outbreaks of vector-borne pathogens and their environmental drivers is typically complicated, nonlinear, and may vary by taxonomic units below the species level (e.g., strain or serotype). Here, we aim to untangle how these complex forces shape the risk of outbreaks of Bluetongue virus (BTV); a vector-borne pathogen that is continuously emerging and re-emerging across Europe, with severe economic implications. We tested if the ecological predictors of BTV outbreak risk were serotype-specific by examining the most prevalent serotypes recorded in Europe (1, 4, and 8). We used a robust machine learning (ML) pipeline and 23 relevant environmental features to fit predictive models to 24,245 outbreaks reported in 25 European countries between 2000 and 2019. Our ML models demonstrated high predictive performance for all BTV serotypes (accuracies > 0.87) and revealed strong nonlinear relationships between BTV outbreak risk and environmental and host features. Serotype-specific analysis suggests, however, that each of the major serotypes (1, 4, and 8) had a unique outbreak risk profile. For example, temperature and midge abundance were as the most important characteristics shaping serotype 1, whereas for serotype 4 goat density and temperature were more important. We were also able to identify strong interactive effects between environmental and host characteristics that were also serotype specific. Our ML pipeline was able to reveal more in-depth insights into the complex epidemiology of BTVs and can guide policymakers in intervention strategies to help reduce the economic implications and social cost of this important pathogen.

A study of the composition of the Obsoletus complex and genetic diversity of Culicoides obsoletus populations in Spain.

BACKGROUND: The Culicoides obsoletus species complex (henceforth 'Obsoletus complex') is implicated in the transmission of several arboviruses that can cause severe disease in livestock, such as bluetongue, African horse sickness, epizootic hemorrhagic disease and Schmallenberg disease. Thus, this study aimed to increase our knowledge of the composition and genetic diversity of the Obsoletus complex by partial sequencing of the cytochrome c oxidase I (cox1) gene in poorly studied areas of Spain. METHODS: A study of C. obsoletus populations was carried out using a single-tube multiplex polymerase chain reaction (PCR) assay that was designed to differentiate the Obsoletus complex sibling species Culicoides obsoletus and Culicoides scoticus, based on the partial amplification of the cox1 gene, as well as cox1 georeferenced sequences from Spain available at GenBank. We sampled 117 insects of the Obsoletus complex from six locations and used a total of 238 sequences of C. obsoletus (ss) individuals (sampled here, and from GenBank) from 14 sites in mainland Spain, the Balearic Islands and the Canary Islands for genetic diversity and phylogenetic analyses. RESULTS: We identified 90 C. obsoletus (ss), 19 Culicoides scoticus and five Culicoides montanus midges from the six collection sites sampled, and found that the genetic diversity of C. obsoletus (ss) were higher in mainland Spain than in the Canary Islands. The multiplex PCR had limitations in terms of specificity, and no cryptic species within the Obsoletus complex were identified. CONCLUSIONS: Within the Obsoletus complex, C. obsoletus (ss) was the predominant species in the analyzed sites of mainland Spain. Information about the species composition of the Obsoletus complex could be of relevance for future epidemiological studies when specific aspects of the vector competence and capacity of each species have been identified. Our results indicate that the intraspecific divergence is higher in C. obsoletus (ss) northern populations, and demonstrate the isolation of C. obsoletus (ss) populations of the Canary Islands.

Global emergence and evolutionary dynamics of bluetongue virus.

Bluetongue virus (BTV) epidemics are responsible for worldwide economic losses of up to US$ 3 billion. Understanding the global evolutionary epidemiology of BTV is critical in designing intervention programs. Here we employed phylodynamic models to quantify the evolutionary characteristics, spatiotemporal origins, and multi-host transmission dynamics of BTV across the globe. We inferred that goats are the ancestral hosts for BTV but are less likely to be important for cross-species transmission, sheep and cattle continue to be important for the transmission and maintenance of infection between other species. Our models pointed to China and India, countries with the highest population of goats, as the likely ancestral country for BTV emergence and dispersal worldwide over 1000 years ago. However, the increased diversification and dispersal of BTV coincided with the initiation of transcontinental livestock trade after the 1850s. Our analysis uncovered important epidemiological aspects of BTV that may guide future molecular surveillance of BTV.

Identifying Spanish Areas at More Risk of Monthly BTV Transmission with a Basic Reproduction Number Approach.

Bluetongue virus (BTV) causes a disease that is endemic in Spain and its two major biological vector species, C. imicola and the Obsoletus complex species, differ greatly in their ecology and distribution. Understanding the seasonality of BTV transmission in risk areas is key to improving surveillance and control programs, as well as to better understand the pathogen transmission networks between wildlife and livestock. Here, monthly risk transmission maps were generated using risk categories based on well-known BTV R0 equations and predicted abundances of the two most relevant vectors in Spain. Previously, Culicoides spp. predicted abundances in mainland Spain and the Balearic Islands were obtained using remote sensing data and random forest machine learning algorithm. Risk transmission maps were externally assessed with the estimated date of infection of BTV-1 and BTV-4 historical outbreaks. Our results highlight the differences in risk transmission during April-October, June-August being the period with higher R0 values. Likewise, a natural barrier has been identified between northern and central-southern areas at risk that may hamper BTV spread between them. Our results can be relevant to implement risk-based interventions for the prevention, control and surveillance of BTV and other diseases shared between livestock and wildlife host populations.

A model for the assessment of bluetongue virus serotype 1 persistence in Spain.

Bluetongue virus (BTV) is an arbovirus of ruminants that has been circulating in Europe continuously for more than two decades and has become endemic in some countries such as Spain. Spain is ideal for BTV epidemiological studies since BTV outbreaks from different sources and serotypes have occurred continuously there since 2000; BTV-1 has been reported there from 2007 to 2017. Here we develop a model for BTV-1 endemic scenario to estimate the risk of an area becoming endemic, as well as to identify the most influential factors for BTV-1 persistence. We created abundance maps at 1-km2 spatial resolution for the main vectors in Spain, Culicoides imicola and Obsoletus and Pulicaris complexes, by combining environmental satellite data with occurrence models and a random forest machine learning algorithm. The endemic model included vector abundance and host-related variables (farm density). The three most relevant variables in the endemic model were the abundance of C. imicola and Obsoletus complex and density of goat farms (AUC 0.86); this model suggests that BTV-1 is more likely to become endemic in central and southwestern regions of Spain. It only requires host- and vector-related variables to identify areas at greater risk of becoming endemic for bluetongue. Our results highlight the importance of suitable Culicoides spp. prediction maps for bluetongue epidemiological studies and decision-making about control and eradication measures.

The possible route of introduction of bluetongue virus serotype 3 into Sicily by windborne transportation of infected Culicoides spp.

In October 2017, the first outbreak of bluetongue virus serotype 3 (BTV-3) began in Italy, specifically in western Sicily. The route of entrance remains unclear, although since 2016 the same strain had been circulating only 150 km away, on the Tunisian peninsula of Cape Bon. The present analysis assessed the feasibility that wind could have carried BTV-3-infected Culicoides spp. from Tunisia to Sicily. An advection-deposition-survival (ADS) model was used to estimate when and where Culicoides spp. were likely to be introduced prior to the first BTV-3 report in Italy. Additionally, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to support ADS outputs. The modelling suggests that during September 2017, strong wind currents and suitable climatic conditions could have allowed the transportation of Culicoides spp. from BTV-3-infected areas in Tunisia into Sicily. ADS simulations suggest that particles could have reached the province of Trapani in western Sicily on 2 and 12 September. These simulations suggest the feasibility of aerial transportation of infected Culicoides spp. from Tunisia into Sicily. They demonstrate the suitability of the ADS model for retrospective studies of long-range transportation of insects across large water bodies, which may enhance the early detection of vectorial disease introduction in a region.

An advection-deposition-survival model to assess the risk of introduction of vector-borne diseases through the wind: Application to bluetongue outbreaks in Spain.

This work develops a methodology for estimating risk of wind-borne introduction of flying insects into a country, identifying areas and periods of high risk of vector-borne diseases incursion. This risk can be characterized by the role of suitable temperatures and wind currents in small insects' survival and movements, respectively. The model predicts the number density of introduced insects over space and time based on three processes: the advection due to wind currents, the deposition on the ground and the survival due to climatic conditions. Spanish livestock has suffered many bluetongue outbreaks since 2004 and numerous experts point to Culicoides transported by wind from affected areas in North Africa as a possible cause. This work implements numerical experiments simulating the introduction of Culicoides in 2004. The model identified southern and eastern Spain, particularly between June and November, as being at greatest risk of wind-borne Culicoides introduction, which matches field data on bluetongue outbreaks in Spain this year. This validation suggests that this model may be useful for predicting introduction of airborne pathogens of significance to animal productivity.