Veterinaria Italiana (Journal)
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O6-3 Machine Learning for MALDI-TOF MS identification of Brucella
No full textMALDI-TOF mass spectrometry (MS) is a fast and reliable method for bacterial identification widely used. Most common databases used for this purpose lack reference profiles for Brucella species, hampering the correct species identification and reliable sub-species characterization. Here, we report the creation of peptide mass reference spectra, which were used to train a machine learning (ML) algorithm for predicting Brucella species. We selected two datasets composed of 107 Brucella strains for the ML alghoritm training, and 160 Brucella strains for validation. The strains have been isolated from our diagnostic activities and included: B. melitensis bv 3 and bv1, B. abortus bv 1 and bv3; B. suis bv 2, B. ceti and B. ovis. All strains were typed by means of molecular and biochemical analyses. Strains were heat inactivated, and protein extraction was performed using ethanol-formic acid protocol. The samples were spotted on a 96-spot steel plate target and covered with alpha-cyano-4-hydroxy-cinnamic acid (HCCA) matrix solution (Bruker Daltonics) before MALDI-TOF MS analysis. The ML algorithm XGBoost was trained with features engineered from mass spectra observations related to the corresponding Brucella species. We used a 5-fold cross-validation, repeated 10 times, to evaluate 50 models whose hyper-parameters were selected according to the random search procedure for determining the greatest accuracy model. Subsequently, the selected model was validated by testing 480 samples from the validation dataset. The Brucella species identification had 99.4% accuracy with 100% diagnostic sensitivity (dSe) and specificity (dSp) for B. abortus, B. ceti and B. ovis. However we observed a small decrease of performance for B. melitensis and B. suis bv2 which showed 97.2% dSe and 99.2% dSp, respectively. Overall, the ML algorithm misidentified 3 B. melitensis with B. suis bv2. Our results showed that MALDI-TOF MS is reliable for Brucella identification to the species level from culture plates. The trained ML algorithm revealed to be specific and highly sensitive and appears to be an efficient and reproducible method for the rapid detection of the genus Brucella. Considering the presence of at least 12 Brucella species, the preliminary dataset needs to be enlarged for comprehensive representation of the entire genus
P6-04 Livestock brucellosis monitoring using bacteriological method in Ukraine for the period 2017-2021
No full textThe epidemiological and epizootic brucellosis situation remains tense in many countries around the world. The territory of Ukraine is free of brucellosis in farm and domestic animals. However, there is a risk of contracting the disease from Russia, Georgia, Turkey, and other countries during export and import operations. The aim of this study was to survey for brucellosis in livestock farms and wild faun, and to assess the epizootic situation of brucellosis in Ukraine. The study was based on biological material from seropositive animals, aborted fetuses, stillbirths, and game meat samples. Brucellosis was monitored using bacteriological methods in accordance with the WOAH Terrestrial Manual 2009. Chapter 2.7.2. Caprine and ovine brucellosis (excluding Brucella dependent), WOAH Terrestrial Manual Chapter 2.7.8 and “Guidelines for brucellosis diagnosis in animals”, approved by the Ministry of Agro-Industrial Complex of Ukraine, State Department of Veterinary Medicine in 1998. For the period 2017-2021, the regional state laboratories of the State Service of Ukraine on Food Safety and Consumers Protection and the State Scientific and Research Institute of Laboratory Diagnostics and Veterinary and Sanitary Expertise tested 1,327 samples of biological material (aborted fetuses, stillbirths, game meat samples to rule out brucellosis). In 2017, 542 samples [(cattle - 416 samples (76.8%), pigs - 109 samples (20%), small cattle - 17 samples (3%)]. In 2018 - 160 samples [(cattle - 116 samples (72.5%), pigs - 39 samples (24.4%), small cattle - 5 samples (3%)]. In 2019, 210 samples [(15.8%) (cattle - 178 samples) (84.8%), pigs - 29 samples (13.8%), small cattle - 3 samples (1.4%)]. In 2020, 240 samples [(cattle - 175 samples (72.9%), pigs - 57 samples (23.8%), small cattle - 8 samples (3.3%)]. During 2021 - 175 samples [(cattle - 122 samples (69.7%), pigs - 49 samples (28%), small cattle - 4 samples (2.3%)]. No brucellosis pathogens were detected using bacteriological testing or guinea pig bioassays. Giv-n the danger of brucellosis to human health, through transmission of the pathogen through unpasteurized milk, cheese, butter, uncooked meat and offal, monitoring of livestock for brucellosis is mandatory
O8-4 Outbreak of bovine brucellosis in the Bargy mountain: special feature of B. melitensis infection in cattle
No full textSince France is officially free of bovine brucellosis, a single autochthonous outbreak of cattle brucellosis occurred in 2012 in the Bargy, linked to a wild reservoir of B. melitensis in Alpine ibex. On October 12, 2021, a positive ELISA result on pooled milk sample was confirmed during the monthly serological monitoring in a farm of 240 dairy cattle. Two cows presented a positive reaction after brucellin testing and one of them was seropositive. Post-mortem investigations of both cows led to the isolation of three Brucella melitensis isolates from the lymph nodes and milk of a primiparous cow that calved on September 14, 2021 and grazed in 2020 in the Bargy mountain pasture. Sequencing of the 3 isolated strains demonstrated the great genomic proximity with the strains of the 2012 cattle household and those isolated from ibex since 2012. The slaughter of the herd in January 2022 mobilized 12 people on site for 2 days for the collection of 1516 samples (1462 samples from 214 cattle; 54 samples from 18 euthanized calves). Bacteriological cultures were performed from blood, three pairs of lymphnodes, spleen, genital swab and genital tract. Samples were processed by the entire network of approved local laboratories with negative results. Three females presented non-negative serological results at slaughter (1 Rose Bengal (RB) positive, 1 RB and Complement fixation (CF) positive, 1 doubtful ELISA). Additional analyses are underway (molecular analyses, enriched cultures), in order to investigate animals that have presented particular risk factors (abortion, FC or ELISA result close to the threshold, taking colostrum from the infected cow, transhumance). Current results are in favor with a recent infection, together with a limited intra-herd dissemination.
The authors would like to thank the French authorities, the laboratory network (LDA 73, LDA 01, LDA 13, EVA (LDA 31), LABOCEA, PUBLIC LABOS (LDA TARN), TERANA), the slaughterhouse and staff who contributed to the investigations.
Contact author: [email protected]
P5-05 Effectiveness of Brucella abortus S19 and RB51 vaccine strains: a systematic review
No full textBrucella abortus S19 and RB51 are the most used vaccines to control bovine brucellosis worldwide; therefore, this study aimed to perform a systematic review on the effectiveness of these two vaccine strains (field trials to evaluate bovine brucellosis vaccines efficacy). The literature review was conducted on April 03rd, 2021 on five databases (CABI, Cochrane, PubMed, Scielo, Scopus and Web of Science) and included papers published between 1976 and 2014. The search strategy recovered a total of 6027 papers, which after selection based on title, abstract, full-text and considering the eligibility criteria ended up with 17 papers and 33 trials included. The strain most used in the trials was S19 (25/33, 75.75%), at the dose of 109 colony forming units (CFU) (18/25, 72%), by subcutaneous route (21/25, 84%), in a single dose (20/25, 80%), and in adult animals (23/25, 92%). RB51 was used in 8 (24.24%) trials, %), at the dose of 109 colony forming units (CFU)(5/8, 62.5%), mainly by subcutaneous route (8/8, 100%), in a single dose (4/8, 50%) and animals younger than 12 months (6/8, 75%). The higher field challenge (non-experimental) observed was 39% and the lower, 0.64% (mean 13.36% ± 12.89%). The incidence of brucellosis and/or abortion were the outcomes assessed in 12 (36.36%) trials, whereas reduction of abortion rate and/or reduction in brucellosis prevalence were the outcomes assessed in 21 trials (63.63%). The most used serology test to evaluate brucellosis infection was Complement Fixation test (26/33, 78.78%), followed by Rivanol test (24/33, 72.72%). The great heterogeneity in the initial prevalence of the disease before vaccination (natural challenge), in the vaccination protocols and doses used, and in the adoption or not of other control policies associated with vaccination, prevented a meta-analysis of the studies included in the systematic review. In conclusion, the systematicreview results suggest that the S19 at the dose of 109 CFU is effective to reduce brucellosis prevalence in the herds when used in adult animals
O4-3 Use of brucellin skin test to identify water buffaloes (Bubalus bubalis) vaccinated with the live attenuated vaccine B. abortus strain RB51
No full textThe use of the live attenuated Brucella abortus RB51 vaccine in water buffaloes was exceptionally authorised in Italy in 2007 as an additional measure to reduce the impact of Brucellosis in some endemic areas. Vaccination was restricted to prepubescent animals only, to avoid the known risks of excretion with milk or abortion when administered to adult females. To survey non-authorised vaccinations, a diagnostic protocol was developed combining RB51 complement fixation (RB51-CFT) and brucellin skin tests (BST). The aim of the study has been to assess performances (Se and Sp) of BST when applied to a known population of buffaloes vaccinated with RB51, and controls. Data from two different RB51 vaccine trials were combined, and included 49 vaccinated animals and 9 controls. Both trials considered two separate injections of a triple dose of RB51 vaccine to young animals, but age and timing of vaccination and timing of booster dose differed slightly. BST was performed using commercial brucellergene OCB. In the first (long term) trial, BST was performed 3 months after delivery, i.e. 23-25 months after booster vaccination. In the second trial, BST was executed 18 months after the booster dose. Different cut-off for skin thickness increase were considered to evaluate BST performance. In both trials most vaccinated animals showed a BST positive reaction, that was more evident at 72hrs compared to 48hrs, this independently of the vaccination protocol applied. BST showed best value of Se (83,7%, 70,9-91,4 C.I.) and Sp (100%, 71,7-100 C.I.) when considering a skin thickness increase of 1,5 mm as cut-off. As expected, some vaccinated animals (8/49, 16,3%) tested negative to BST. Taken together, both study results indicate that BST, when applied at herd level, is a suitable test to identify RB51 vaccinated animals for long time after vaccination. Conversely, due to the low sensitivity, application of BST on individual animals is questionable
P10.5 Spatial modelling of Aedes caspius (Pallas 1771) and Aedes vexans (Meigen 1830) distribution in the Po Plain (Northern Italy)
No full textIn this work, we analyse the abundance data collected in the frame of West Nile Virus (WNV) surveillance in northern Italy (Po Plain) by 292 CO2-baited traps to evaluate the distribution and density of two non-target mosquito: Aedes caspius (Pallas 1771) and Aedes vexans (Meigen 1830). We applied two different approaches of spatial analysis (geostatistical and machine learning) which gave congruous results. Both species are more abundant in the middle of Po plain, near Po River, but distribution was different. Ae. caspius was more abundant in the east and west areas, Ae. vexans in the middle area of Po plain. This work demonstrated the importance to maintain and improve entomological surveillance of WNV, with an adequate sampling effort
P10.6 Does landscape play a role in the canine leptospirosis epidemic in Sydney?
No full textBeginning in 2017, cases of canine leptospirosis started to be reported from the City of Sydney council area, the highly urbanized centre of the Greater Sydney Area, Australia (Griebsch et al., 2022). Prior to this, the most recent cases had been reported in 1976. The reasons for the appearance of canine leptospirosis after a period of more than 30 years of absence are unknown. Some suggested reasons included local construction activities, flooding events, and rodent problems (Gong et al., 2022). The aim of this research program is to identify the drivers of the current canine leptospirosis epidemic in Sydney, and specifically the role played by the urban landscape.
To date, using passive surveillance of veterinary practices, case ascertainment has been carried out. Using demographic information generated by dog registration and microchip data, the population at-risk has been modelled. Statistical areas have been used to calculate disease incidence. For each of these areas, information on a range of urban landscape factors have been extracted; these include parks, recreational areas, water bodies, vegetation coverage, and housing density.
Between 2017 and 2023, 20 cases of canine leptospirosis have been detected in the two central local government areas of Sydney and Inner West. The greatest number of cases (n=8) were detected in 2019. Cases were reported from a total of 18 out of 906 statistical areas. Clustering of cases was identified in an area on the periphery of the central business district of Sydney in 2019. Both a statistical area matched case-control study analysis and an area-based incidence Poisson regression are being used to identify the landscape factors associated with canine leptospirosis.
Most of our knowledge of landscape factors that influence leptospirosis outbreaks and epidemic are derived from the human literature and are often anecdotal. Results of the current study are expected to assist dog owners and veterinarians to control and prevent canine leptospirosis within this region
R04.3 Reducing uncertainty in spatial analysis involving fragmentated farms
No full textFarm fragmentation refers to spatial disaggregation of a farm into smaller, often highly separated parcels of land. Ireland has a high proportion of fragmented farms; an issue not unique to Ireland. Spatial analysis of farms depends on assigning a location to the livestock. Where a farm is heavily fragmented, this becomes problematic and introduces uncertainty. With increasingly sophisticated analytical techniques, reducing this uncertainty is imperative. We explore techniques to quantify the extent and regional variation in fragmentation and the between-fragment distances of fragmented farms in Ireland. We then explore methodologies to help account for farm fragmentation in geospatial analysis and to assist in surveillance and field epidemiology.
Farms in Ireland are recorded on a Land Parcel Identification System (LPIS) allowing for interrogation by GIS. The most commonly used point representation of a farm is the centroid of the largest single fragment. However, laneways, roads, streams and other physical features break up farms on a GIS so they are not seen as a genuine spatial/epidemiological unit. Alternative spatial representations such as weighted centroid, geographic medians, density based clustering, etc. are useful but often place the estimated location outside of the actual farm. This becomes increasingly problematic as fragment separation distances become greater. To better represent farms split by local features, we utilised the Integrate tool (ArcGIS 10.7, ESRI Redlands CA) to allow same-farm boundaries to snap together across these features. For fragmented farms, the largest integrated fragment was assigned as ‘home’ and all other integrated fragments were assigned as ‘away’. Distance metrics were calculated from ‘home’ to ‘away’ fragments creating a fragment profile.
A methodology to better describe contiguity between farms was devised. Integrated farm fragments were placed into 3 categories based on their relative size to total farm size; A=>50%, B=20%-50%, C=<20%. A scoring matrix was generated to define the relative importance of adjacency between fragments based on their size category; A-A=1, A-B=2, B-B=3, A-C=4, B-B=5, C-C=6.
A spatial profile was generated for farms based on the number of integrated fragments and the fragment-fragment distances. Summary metrics (and maps) were generated by County and by uniform hexagonal grid.
A Neighbourhood matrix was generated for all contiguous between-farm fragments and coupled with shared boundary distance. This was appended to additional farm information such as head count, stocking densities, Bovine Tuberculosis (bTB) testing history and farm enterprise type to assist in prioritising farm surveillance in bTB outbreaks.
Farm fragmentation in Ireland was quantified and described through distance and neighbourhood metrics allowing for greater accuracy in application of exposure variables in geospatial analytics. In addition, they aid in prioritisation of epidemiological field investigations and contiguous surveillance for bTB and other transmissible diseases of livestock
R10.6 Surveillance data and early warning models of highly pathogenic avian influenza in East Asia, 2020-2022
No full textAvian influenza poses a problem for animal welfare and potentially also for public health. In recent years, the numbers of disease outbreaks and cases of highly pathogenic avian influenza (HPAI) virus infection throughout the world has increased, resulting in many poultry flocks being culled. Preventive actions require timely knowledge of high-risk infection periods. Thus, there is an increasing need for early warning systems.
We utilized readily available data from the World Organization for Animal Health and the Food and Agriculture Organization of the United Nations on HPAI H5 detections in wild and domestic birds together with a time-series modelling framework (Meyer et al., 2014) to predict HPAI detections within East Asia. This framework decomposes time series data into endemic and epidemic components, and we have previously used it successfully to model HPAI in Europe. We tested multiple model formulations, including seasonality, long-distance versus short-distance transmission, and covariates (coastline length and area of wetlands).
Due to data constraints, we were only able to fit a model for Japan and South Korea for the years 2020-2022, as these two countries consistently reported outbreaks during the study period. We divided each country into regions based on geography and local administrative areas. The best performing model included seasonality in both the endemic and epidemic components, and covariates as offsets in the endemic component. Due to data constraints, we did not differentiate between H5 subtypes. The model was fitted to all HPAI records with good overall agreement. Interestingly, we found a clear cyclic seasonal component in the predicted occurrence of HPAI, whereas our previous model for Europe for the same time period predicted occurrence during the summer period as well, i.e., less cyclic seasonality. The model predicted that most reported detections (79.3%) were epidemic in nature and were dominated by within-region transmission (55.5%). Of the reported detections, 20.7% was described as endemic transmission in the model, which could represent transmission from migratory birds coming from regions and countries not included in our study and/or endemic H5 HPAI virus circulating within the included regions.
The modelling framework used in this study produced a model that was able to predict H5 HPAI detections in Japan and South Korea on a weekly basis and could be utilized by decision makers to predict periods with higher risk of HPAI within these countries. If possible, future modelling studies should obtain more data from other Asian countries to create a combined model for Asia/Oceania. Given enough surveillance data, future studies could also focus on modelling the specific HPAI genotypes and clades; this would potentially improve predictive ability and timeliness, as well as clarify endemic and epidemic components of HPAI occurrence
P10.13 Using a spatially-explicit agent-based disease spread model to identify high risk areas and most cost-effective control strategies in the scenario of potential introduction of Chronic Wasting Disease in California
No full textChronic wasting disease (CWD) is an always fatal, neurodegenerative prion disease of cervids. In North America, it affects white-tailed deer (Odocoileus virginianus), mule deer (Odocoileus hemionus), Rocky Mountain elk (Cervus elaphus nelsoni), moose (Alces alces shirasi), and reindeer/caribou (Rangifer tarandus). The disease spreads through direct contact with infected individuals or indirect contact with infectious material (e.g., fluids, feces, and tissues of infected animals). First recognized in Colorado in 1967, CWD has incessantly spread across North America having been detected in captive or free-ranging cervids in at least 30 states of the US, 4 Canadian provinces, Norway, Sweden, Finland, and South Korea. In free-ranging populations, uncontrolled CWD expansion leads to geographical spread, increased prevalence, reduced adult survival rates, and destabilization of population dynamics. While CWD has never been detected in California’s cervid populations despite surveillance efforts since 1999, the constant threat remains, as the disease could be introduced at any time through natural or anthropogenic movement of infected animals or materials. This study utilizes an agent-based modeling approach and integrates real mule deer population data, to simulate 5-year epidemiological expansion of hypothetical CWD introductions within the deer conservation units (DCUs) of California. Environmental factors such as wildfires and the presence of various scavenger species were also incorporated. Multiple scenarios were modeled, considering different deer densities and proposed management measures, including surveillance for initial detection, prompt response to the first detection, strategic harvest management, targeted culling, and carcass management. The model assesses the effectiveness of these management strategies, providing estimates of disease transmission probabilities, impacts on deer herds, and population dynamics for each DCU under various disease introduction scenarios. The model also quantifies the direct and indirect transmission events, as well as the probability of CWD infection across different deer populations. The findings contribute to evidence-based decision-making by providing valuable information to support the design of cost-effective surveillance and management strategies to better prevent and rapidly detect and control CWD in the case of a potential introduction in California