Preventive veterinary medicine最新文献

African Swine Fever (ASF) is a contagious viral disease that infects wild and domesticated swine. In early 2022, the virus was found in wild boar in the Apennine mountains of mainland Italy.² Since then, it has spread from wild boar to domesticated swine. To control the spread of ASF, an effective surveillance system and the implementation of strict biosecurity measures on farms are required yet are unevenly implemented across husbandry systems. Smallholder farms in particular are known to have low levels of biosecurity. In the Apennine mountains of Italy, small commercial farms have been found to have low levels of biosecurity despite being located in areas with high densities of wild boar, and, hence, being high-risk sites for potential ASF incursion and subsequent diffusion. To address the question as to why the level of biosecurity is low, interviews and participant observation were conducted with smallholder commercial farmers. The interviews identified the social, cultural, and ecological factors that affect the implementation of biosecurity measures in small commercial swine farms in the Apennines. Farmers expressed knowledge of priority biosecurity measures and an overall willingness to follow rules and regulations; however, the application of the measures in practice was uneven across farms. Economic, political, and ecological factors as well as farmer beliefs about biosecurity emerged as important factors affecting the implementation of biosecurity measures. These include economic constraints, challenges posed by the mountain environment, a shifting regulatory environment, and ideas about animal welfare. Other important factors include cultural factors such as the use of traditional agricultural methods and norms about customer access to animals, time constraints and the perceived hassle of implementing the measures, farmer age, farmer relationships with government officials and veterinarians, and the role of pigs in reducing farm waste. The study confirmed that wild boar are present in high numbers and in close proximity to smallholder commercial farms in the Apennines.

Despite the prevalence of co-infections and the association of over 50 viral and 46 bacterial pathogens with pig diseases, little is known about their simultaneous occurrence, particularly in commercial pig farming environments where health programs are in place. To address this knowledge gap, this study aimed to evaluate the pathogen threshold of respiratory and enteric pathogens in pig herds using the Pork MultiPath™ (PMP1 and PMP2, respiratory and enteric respectively) technology, which detects multiple pathogens simultaneously in a single reaction with high sensitivity and specificity. In this study the most prevalent respiratory pathogens, Mycoplasma hyrohinis, Pasteurella multocida, and Haemophilus parasuis detected by PMP1 were effectively controlled during the nursery stage through strategic treatment with tiamulin. Even though the major respiratory incidences were reduced, the recorded coughing and sneezing rates were associated with the levels of H. parasuis and M. hyrohinis, which were set at 1356 and 1275 copies/reaction, respectively. In addition, one of the identified co-infection patterns indicated a strong relationship between the occurrence of H. parasuis and M. hyorhinis at the sample and pen levels, highlighting the high likelihood of detecting these two pathogens together. Testing with enteric panel PMP2 revealed that the most frequently detected virulence factors during the early nursery stage were Escherichia coli genes for toxins - ST1, ST2, and fimbriae – F4 and F18. Moreover, a co-infection with Rotavirus B and C was often observed during the nursery stage, and a strong positive correlation between these two markers has been identified. Additionally, the levels of several markers, namely E. coli F4, F5, F18, LT, ST1, and ST2, have been associated with a higher likelihood of sickness in pig populations. In addition, the onset of Brachyspira pilosicoli during the nursery and grower stages was found to be associated with an increased risk of diarrhoea, with a set threshold at around 500 copies/reaction. Although simultaneous detection of multiple pathogens is not yet widely used in the pig industry, it offers a significant advantage in capturing the diversity and interactions of co-infections. Testing pooled samples with Pork MultiPath™ is cost-effective and practical to regularly monitor the health status of pig populations.

Digital dermatitis (DD) is a bovine claw disease responsible for ulcerative lesions on the planar aspect of the hoof. DD is associated with massive herd outbreaks of lameness and influences cattle welfare and production. Early detection of DD can lead to prompt treatment and decrease lameness. Computer vision (CV) provides a unique opportunity to improve early detection. The study aims to train and compare applications for the real-time detection of DD in dairy cows. Eight CV models were trained for detection and scoring, compared using performance metrics and inference time, and the best model was automated for real-time detection using images and video. Images were collected from commercial dairy farms while facing the interdigital space on the plantar surface of the foot. Images were scored for M-stages of DD by a trained investigator using the M-stage DD classification system with distinct labels for hyperkeratosis (H) and proliferations (P). Two sets of images were compiled: the first dataset (Dataset 1) containing 1,177 M0/M4H and 1,050 M2/M2P images and the second dataset (Dataset 2) containing 240 M0, 17 M2, 51 M2P, 114 M4H, and 108 M4P images. Models were trained to detect and score DD lesions and compared for precision, recall, and mean average precision (mAP) in addition to inference time in frame per second (FPS). Seven of the nine CV models performed well compared to the ground truth of labeled images using Dataset 1. The six models, Faster R-CNN, Cascade R-CNN, YOLOv3, Tiny YOLOv3, YOLOv4, Tiny YOLOv4, and YOLOv5s achieved an mAP between 0.964 and 0.998, whereas the other two models, SSD and SSD Lite, yielded an mAP of 0.371 and 0.387 respectively. Overall, YOLOv4, Tiny YOLOv4, and YOLOv5s outperformed all other models with almost perfect precision, perfect recall, and a higher mAP. Tiny YOLOv4 outperformed all other models with respect to inference time at 333 FPS, followed by YOLOv5s at 133 FPS and YOLOv4 at 65 FPS. YOLOv4 and Tiny YOLOv4 performed better than YOLOv5s compared to the ground truth using Dataset 2. YOLOv4 and Tiny YOLOv4 yielded a similar mAP of 0.896 and 0.895, respectively. However, Tiny YOLOv4 achieved both higher precision and recall compared to YOLOv4. Finally, Tiny YOLOv4 was able to detect DD lesions on a commercial dairy farm with high performance and speed. The proposed CV tool can be used for early detection and prompt treatment of DD in dairy cows. This result is a step towards applying CV algorithms to veterinary medicine and implementing real-time DD detection on dairy farms.

Livestock keepers who operate on a small scale in the United Kingdom are often described as either smallholders or hobby farmers; however, this is not always the case. There is another distinct population in Scotland. The crofting system promotes the preservation of a way of life that is significant to the cultural heritage of Scotland, whilst at the same time utilising and maintaining marginal land that could otherwise be deemed of very low productive value. We developed two cross-sectional questionnaire surveys to gather descriptive data about individuals from two populations (crofters and smallholders) who kept sheep and/or cattle. Our aim was to explore demographics, animal health, husbandry, and biosecurity practices of these two communities, including how they may interact with other livestock sectors. Most respondents in each population kept sheep, with far fewer keeping cattle. There was a distinct geographical difference in the approximate location of respondents’ holdings. Movement of sheep was often local, temporary, and exempt from reporting to national databases. Visits from the vet were infrequent, but the vet remained an important source of animal health advice, alongside peer networks. The information from these surveys is valuable because policy decisions taken with predominantly larger, commercial-scale enterprises in mind also frequently apply to small-scale enterprises, even though these smaller enterprises may not have the same opportunity to influence those decisions or implement the requirements. Aspects of agricultural activity and food production at the scale explored in these surveys – including plurality of employment and diversification away from purely agricultural activities – are relevant to the United Nations Sustainable Development Goals of sustainable cities and communities, zero hunger and life on land. In this context, competent authorities should support this type of context-sensitive agriculture, alongside seeking to maintain animal health and welfare standards at the highest possible level on a national scale. Our surveys contribute to improved understanding of how these enterprises function and therefore will support policy makers when considering the breadth of keepers and circumstances affected by rules and regulations governing agriculture.

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.

On pig farms ample opportunity exists for pig-to-human and human-to-pig (cross-species) influenza transmission. The purpose of this study was to assess the risks of cross-species influenza transmission within an indoor pig grower unit in the United States and to prioritize data gaps. Using the World Organization for Animal Health risk assessment framework we evaluated influenza transmission across two risk pathways: 1. What is the likelihood that based on current conditions on a single typical hog grower-finisher facility in the Midwest (US), during a single production cycle, at least one hog becomes infected with an influenza virus associated with swine (either H1N1, H3N2, or H1N2) [step 1a] and that at least one worker becomes infected as a result [step 1b] and that the worker develops symptoms [step 1c]? And 2. What is the likelihood that, based on current conditions on a single typical hog grower-finisher facility in the Midwest (US), during a single production cycle, at least one worker becomes infected with an influenza virus associated with people (either H1N1, H3N2, or H1N2) [step 2a] and that at least one pig becomes infected as a result [step 2b] and that the pig(s) develop(s) symptoms [step 2c]?

Semi-quantitative probability and uncertainty assessments were based on literature review including passive and active influenza surveillance data. We assumed a typical pig-grower farm has capacity for 4,000 pigs, two workers, and minimal influenza control measures. Probability and uncertainty categories were assessed for each risk step and the combined risk pathway.

The combined risk assessment for risk pathway one was estimated to be Very Low for H1N1 and H1N2 with an overall High level of uncertainty. The combined risk assessment for risk pathway two was estimated to be Extremely Low for H1N1 and H3N2 with a High degree of uncertainty. Scenario analyses in which influenza control measures were assumed to be implemented separately (implementing vaccinating sows, mass vaccinating incoming pigs or improved personal protective equipment adherence) showed no reduction in the combined risk category. When implementing three influenza control methods altogether, the combined risk could be reduced to Extremely Low for risk pathway one and remained Extremely Low for risk pathway two. This work highlights that multiple influenza control methods are needed to reduce the risks of inter-species influenza transmission on swine farms.

The diagnosis of infectious diseases at herd level can be challenging as different stakeholders can have conflicting priorities. The current study proposes a “proof of concept” of an approach that considers a reasonable number of criteria to rank plausible diagnostic strategies using multi-criteria decision analysis (MCDA) methods. The example of Salmonella Dublin diagnostic in Québec dairy herds is presented according to two epidemiological contexts: (i) in herds with no history of S. Dublin infection and absence of clinical signs, (ii) in herds with a previous history of infection, but absence of clinical signs at the moment of testing. Multiple multiparty exchanges were conducted to determine: 1) stakeholders’ groups; 2) the decision problem; 3) solutions to the problem (options) or diagnostic strategies to be ordered; 4) criteria and indicators; 5) criteria weights; 6) the construction of a performance matrix for each option; 7) the multi-criteria analyses using the visual preference ranking organization method for enrichment of evaluations approach; 8) the sensitivity analyses, and 9) the final decision. A total of nine people from four Québec’s organizations (the dairy producers provincial association along with the DHI company, the ministry of agriculture, the association of veterinary practitioners, and experts in epidemiology) composed the MCDA team. The decision problem was “What is the optimal diagnostic strategy for establishing the status of a dairy herd for S. Dublin infection when there are no clinical signs of infection?”. Fourteen diagnostic strategies composed of the three following parameters were considered: 1) biological samples (bulk tank milk or blood from 10 heifers aged over three months); 2) sampling frequencies (one to three samples collection visits); 3) case definitions to conclude to a positive status using imperfect milk- or blood-ELISA tests. The top-ranking diagnostic strategy was the same in the two contexts: testing the bulk tank milk and the blood samples, all samples collected during one visit and the herd being assigned a S. Dublin positive status if one sample is ELISA-positive. The final decision favored the top-ranking option for both contexts. This MCDA approach and its application to S. Dublin infection in dairy herds allowed a consensual, rational, and transparent ranking of feasible diagnostic strategies while taking into account the diagnostic tests accuracy, socio-economic, logistic, and perception considerations of the key actors in the dairy industry. This promising tool can be applied to other infectious diseases that lack a well-established diagnostic procedure to define a herd status.

To prevent foodborne infections from pigs and cattle, the whole food chain must act to minimize the contamination of products, including biosecurity measures which prevent infections via feed and the environment in production farms. Rodents and other small mammals can be reservoirs of and key vectors for transmitting zoonotic bacteria and viruses to farm animals, through direct contact but more often through environmental contamination. In line with One Health concept, we integrated results from a sampling study of small mammals in farm environments and data from a capture-recapture experiment into a probabilistic model which quantifies the degree of environmental exposure of zoonotic bacteria by small mammals to farm premises. We investigated more than 1200 small mammals trapped in and around 38 swine and cattle farm premises in Finland in 2017/2018. Regardless of the farm type, the most common species caught were the yellow-necked mouse (Apodemus flavicollis), bank vole (Clethrionomys glareolus), and house mouse (Mus musculus). Of 554 intestine samples (each pooled from 1 to 10 individuals), 33% were positive for Campylobacter jejuni. Yersinia enterocolitica was detected in 8% of the pooled samples, on 21/38 farm premises. Findings of Salmonella and the Shiga-toxin producing Escherichia coli (STEC) were rare: the pathogens were detected in only single samples from four and six farm premises, respectively. The prevalence of Campylobacter, Salmonella, Yersinia and STEC in small mammal populations was estimated as 26%/13%, 1%/0%, 2%/3%, 1%/1%, respectively, in 2017/2018. The exposure probability within the experimental period of four weeks on farms was 17–60% for Campylobacter and 0–3% for Salmonella. The quantitative model is readily applicable to similar integrative studies. Our results indicate that small mammals increase the risk of exposure to zoonotic bacteria in animal production farms, thus increasing risks also for livestock and human health.

Wild lagomorphs play a key epidemiological role as reservoirs of Leishmania infantum, causative agent of the largest outbreak of human leishmaniosis in Europe to date. A large-scale survey study was conducted on wild rabbit (Oryctolagus cuniculus) and Iberian hare (Lepus granatensis) populations in Spanish Mediterranean ecosystems to evaluate the exposure of L. infantum and investigate potential risk factors associated with exposure to this zoonotic parasite. Between 2018 and 2021, a total of 631 wild lagomorphs (471 wild rabbits and 160 Iberian hares) were collected in Andalusia (southern Spain) and tested for antibodies against L. infantum using the indirect fluorescent antibody test (IFAT). Spleen samples from 563 of the wild lagomorphs sampled (441 wild rabbits and 122 Iberian hares) were also evaluated by real-time quantitative PCR (qPCR) for detection of Leishmania kDNA. Exposure to L. infantum (positive by IFAT and/or qPCR) was detected in 56.4 % (356/631; 95 %CI: 52.3–60.3) of the lagomorphs analyzed. Anti-Leishmania antibodies were found in 12.8 % (81/631; 95 %CI: 10.2–15.5) of the animals, and L. infantum kDNA was detected in 59.0 % (332/563; 95 %CI: 54.9–63.0) of the spleen samples tested. Phylogenetic analysis revealed high homology (99.9–100 %) between L. infantum sequences obtained and strains previously isolated from humans in Spain. While apparent seroprevalence was significantly higher in Iberian hares (19.4 %; 95 %CI: 13.3–25.5) compared to wild rabbits (10.6 %; 95 %CI: 7.9–13.4), no significant differences in prevalence were found between wild rabbits (61.0 %; 95 %CI: 56.5–65.6) and Iberian hares (51.6 %; 95 %CI: 42.8–60.5). At least one positive animal was found on 64.8 % (70/108) of the hunting grounds sampled, and a high-risk spatial cluster (P < 0.001) was also identified in central Andalusia. The multivariable analysis identified bioclimatic level (meso-Mediterranean climate) and the presence of goats on hunting grounds as risk factors potentially associated with L. infantum exposure in wild lagomorphs. This study shows high, widespread exposure, but heterogeneous distribution of L. infantum in wild lagomorph populations in Mediterranean ecosystems in southern Spain. The results point to the need to promote integrated surveillance programs for the detection of Leishmania spp. in wild lagomorphs in order to establish effective control measures against human leishmaniosis under a One Health approach.

Osteochondrosis (OC) is a focal disturbance of endochondral ossification due to a failure of blood supply to the epiphyseal growth cartilage. In dogs, OC most commonly affects the shoulder joint, followed by the elbow, tarsal, and stifle joints. The condition is associated with clinical signs such as lameness and pain and the prognosis varies depending on the affected joint. Most epidemiologic studies of OC in dogs were performed over 20 years ago, and updated estimates of disease incidence are lacking. Therefore, the objectives of this study were to provide population-based estimates of the incidence rate, cause-specific mortality rate, and age at diagnosis of appendicular OC (AOC, including OC of the shoulder, elbow, stifle, and tarsal joints) and stifle and tarsal OC separately, using data from Agria Djurförsäkring in Sweden (2011–2016). Further, the study aimed to evaluate the risk of OC in subgroups divided by breed and sex and describe previous, concurrent, and subsequent diagnoses of the affected joint in dogs with stifle or tarsal joint OC. The study population included just over 600,000 dogs, of which 685 were affected by AOC. Stifle joint OC (n = 113) was more common than tarsal joint OC (n = 80). The incidence rate of AOC was 3.77 (95% confidence interval (CI): 3.49–4.07) cases per 10,000 dog-years at risk, while the incidence rate of stifle and joint tarsal OC was 0.64 (95% CI: 0.53–0.77) and 0.43 (95% CI: 0.34–0.54) cases per 10,000 dog-years at risk, respectively. All breeds at increased risk of AOC were large or giant, and male dogs had an increased risk of AOC compared to female dogs (RR 1.76, 95% CI: 1.50–2.07, p < 0.001). The median age at first diagnosis during the study period was 0.74 (0.32–11.5) years for AOC, 2.62 (0.45–8.82) years for stifle joint OC, and 0.73 (0.35–7.35) years for tarsal joint OC. Of the dogs with stifle or tarsal joint OC, 30.2% and 15.0% had a previous diagnosis of stifle/tarsal joint pain or other unspecific clinical signs, respectively, and 13.8% of the dogs with stifle joint OC suffered subsequent cruciate ligament rupture. Osteochondrosis was the most common reason for euthanasia in the affected dogs. In total, 77 dogs were euthanised due to AOC during the study period.