Veterinaria Italiana (Journal)
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O8-6 Insights into the seroprevalence of Brucella canis infection in dogs in Portugal
No full textIn Portugal canine brucellosis due to Brucella canis has historically not been regarded as endemic, but little is known about the real epidemiologic situation. Laboratory testing is only done in animals traveling from Portugal to endemic areas, or after veterinarian request following clinical symptoms. Serological positive reactions are rarely reported, and B. canis has never been isolated in Portugal. As it is not considered a notifiable disease, no official prevalence data are available, representing a challenge for studying its epidemiology. The purpose of this retrospective study was to get insights into the occurrence of B. canis in dogs in Portugal. We screened a collection of serum samples received between 2014 and 2021 at the National animal health reference laboratory (INIAV) for serological control. The collection includes 642 samples from different regions in Portugal, including Azores and Madeira. All samples were tested by complement fixationtest (based on B. ovis antigen, CFT-B. ovis); from these, 438 samples were also tested by rapid slide agglutination test (RSAT and ME-RSAT) and/or immunochromatographic test (ICT). In addition, 252 samples from dogs showing clinical symptoms (including blood samples, vaginal swabs, aborted fetus) were submitted for detection of Brucella spp. with polymerase chain reaction (PCR). There was no positive serology for smooth Brucella spp. The frequency of positive serologic results was 9,7% (62 in 642 dogs) using CFT-B. ovis, but 7,3% of the tested samples showed anti-complementary reaction. From the 62 positive dogs, 21 tested positive in RSAT, ME-RSAT and/or ICT. Regarding the samples submitted to PCR, 19% (48/252) resulted positive. Although the results obtained belong to pre-selected samples (not reflecting the national occurrence) collected from a heterogeneous group of dogs, this first preliminary results suggest a low seroprevalence of B. canis infection in Portugal. The study still ongoing in order to increase the number of samples, the geographic coverage and the evaluation of breeding kennels across Portugal
P2-08 Brucella melitensis immunoproteomics reveal strain-specific antigens suitable for the unbiased diagnosis in a diva strategy
No full textIn the Mediterranean area, Brucella melitensis is among the most common species with sheeps and goats being the major reservoir of the pathogen. Diagnostic and prophylactic measures adopted for the control of brucellosis suffer from a lack of specificity and cross-reactivity, respectively. Also, live-attenuated B. melitensis Rev.1 administration in goat and sheep showed residual virulence and cross-reactivity withthe current diagnostics, raising the need for studies to amend both the vaccinal and diagnostic strategies. Here, we employed an immunoproteomic approach to underline the specific immunogenic proteins of both B. melitensis Rev.1 and B. melitensis 16M strains, meant respectively as the reference of the vaccinal and field strains, and discriminate their antigens from each other and from those of the most cross-reactive specimens (i.e. Escherichia coli O157:H7 and Yersinia enterocolitica O:9). Following the optimized resolution of the Brucellae proteomes, independent incubation of the bacterial protein profiles with properly depleted sera from either Rev.1 or 16M infected animals revealed a distinct map of immunogenic proteins for the assayed bacteria. Differential immunogenic proteins have been identified both computationally and analytically via MS/MS, yielding a list of suitable candidates for the design of novel diagnostics and/or prophylactic strategies. Universal stress protein and related nucleotide-binding proteins (Uspa), nucleoside diphosphate kinase and putrescine utilization regulator were uniquely identified as predictive for the presence of the vaccinal strain. On the other hand, ABC transporter-substrate-binding protein (cluster4_ leucine/isoleucine/valine/benzoate, broad-specificity amino acid ABC transporter-substrate-binding protein) and ABC transporter-substrate-binding pro (cluster1maltose/g3p/polyamine/iron) were identified as putative biomarkers of B. melitensis 16M. The independent identification of the immunogenic proteins confirms previous in-silico bioinformatic prediction performed on the same bacterial strains. Sorting the major immunogenic proteins of both the vaccinal and field strains is pivotal for the design of unbiased serodiagnostic strategies crowning the conventional diagnostics for Brucella. Also, these may serve as the starting point for drawing optimized prophylactic strategies targeting other molecules than those addressed by the diagnostic tests; thus, differentiating infected from vaccinated animals. This improves monitoring campaigns which represents the keystone for the fair and efficient control and eradication of brucellosis in animals and humans
K5 The bumpy road to a Brucella vaccine: the near hits and good catches
No full textExactly ninety-nine years ago, Matilda’s milk and Dr. John Buck’s laboratory accident became one of the most important strides in the history of Brucella vaccines and is considered a success story where “chance favored the prepared mind”. Despite that a century has passed, to this date, there have been no significant advances in the development of superior vaccines, and S19 is still considered to be the best choice in terms of protective efficacy against B. abortus. Here, we discuss lessons learned as well as new approaches to better understand the mechanisms behind enhancing protective efficacy and safety, and their importance in the development of next generation vaccines
R02.2 Epidemic intelligence data and disease risk mapping: the case of Crimean-Congo haemorrhagic fever (CCHF)
No full textThe Epidemic Intelligence from Open Sources (EIOS) (WHO, 2023) is a system used for scanning information from open sources to detect disease events. The initiative, led by the World Health Organization (WHO), was built on the “One Health, All Hazards principle”.
This study uses the EIOS for complementing data collection of neglected diseases thus improving the understanding of their geographic extent and level of risk. A case study on Crimean-Congo Haemorrhagic Fever (CCHF) in the European Region is presented.
Data on CCHF occurrence (January 2012–March 2022) were retrieved using an algorithm to detect mentions of CCHF cases in humans or vectors. A Bayesian additive regression trees (BART) model (Chipman et al., 2010) was built to map the risk of CCHF occurrence. Thirty-three explanatory variables retrieved from WorldClim, ENVIREM and PROBAV_V2.2.1. Every predictor was rescaled at a resolution of 4 x 4 km, aligned, and reprojected to EPSG:4326-WGS84. For each pixel a posterior distribution of predicted probabilities with the associated 95% credible intervals (CI) was obtained. The most important variables were selected by initially fitting the model using all variables. An automated stepwise reduction algorithm with 50 iterations and 10 trees was used to eliminate the variables with the lowest importance and obtain the model with the lowest root mean square error (RMSE). The final model was run with the reduced variable set, 200 trees, and 1000 posterior draws after a burn-in of 100 draws.
Overall, a general decreasing risk trend from south to north across the entire European Region was detected. The results show a positive association between all the temperature-related variables and the probability of CCHF occurrence, with an increased risk in warmer and drier areas. In particular, the Mediterranean basin and the areas bordering the Black Sea are the areas at the highest risk of CCHF occurrence under the current environmental conditions. The model performed well in terms of accuracy (AUC = 0.95), correct classification rate (CCR = 0.80), correct prediction of presences (sensitivity/recall = 0.99), correct prediction of absences (specificity = 0.79), true skill statistic (sTSS = 0.89), Cohen’s kappa (skappa = 0.69), but the positive predictive value was low (precision = 0.31). The low precision means that the model predicts positive pixels where there are no actual observations of human cases or virus isolation from ticks. These are represented by the locations which are likely to be favourable for CCHF occurrence, but where the disease has yet to be reported.
This work demonstrates the potential of internet-based data accessed through the EIOS system to create a disease risk map. The case study presented may constitute a model to combine epidemic intelligence tools and advanced analytical approaches to detect and assess changes in disease distribution, allowing prevention and early mitigation of disease events of veterinary and public health importance
R08.5 Movenet: a toolkit facilitating the use of livestock movement networks in veterinary public health
No full textCommercial livestock movements are an important factor in the spread of infectious diseases of livestock such as African swine fever (ASF). The analysis of these movements can provide valuable intelligence for emergency preparedness: network analysis can determine a movement network’s effectiveness at transmitting infection and the relative importance of different holdings and connections, and incorporating detailed movement data into transmission models can improve the accuracy of predictions such as outbreak size. However, the effective use of livestock movement data is fraught with challenges. On the data side, commercial sensitivity concerns can inhibit data sharing between relevant actors. On the analysis side, network and modelling studies require resources, time and expertise that may be in short supply. We have developed Movenet, a toolkit that addresses these challenges and simplifies the effective use of livestock movement data in veterinary public health.
Movenet is an R package that facilitates the use of livestock movement data in two ways:
To improve the potential for collaborative data sharing by addressing commercial sensitivity concerns, Movenet provides data owners and data managers with a variety of options to make livestock movement data less identifiable: holding identifiers can be substituted (pseudonymised); holding geographical coordinates can be jittered in a density-dependent manner; and movement dates and weights can be modified by addition of random noise or by rounding. Modified datasets can be viewed and downloaded in various formats. The authors encourage users to consider that data modifications may impact the results of any analyses, and to seek an appropriate balance between identifiability of data and accuracy of results. To this end, Movenet provides users with the opportunity to compare the effects of various data modifications on network measures and model predictions.
To facilitate the effective use of (real or modified) movement networks in risk assessment, Movenet provides researchers and decision-makers with a quick entry into social network analysis of livestock movement data. Movenet produces a standardised, downloadable report, with a range of tables and figures displaying metrics relevant to disease spread. The report includes basic network properties such as the number of active holdings and edges (overall and over time), static network analysis of (monthly or yearly) snapshots, temporal analysis including ingoing and outgoing contact chains (reachability), and component analysis. A basic guide to interpretation is also provided. An equivalent quick entry into transmission modelling using livestock movement data is currently in development.
Movenet is freely downloadable, open-source software (https://github.com/digivet-consortium/movenet). An accompanying interactive app is also available, which makes the package functions accessible to users who are not confident in R, or who prefer graphical user interfaces
P04.7 Spatial epidemiology and risk factor analysis of bovine brucellosis in Punjab, Pakistan: a population-based study
No full textBrucellosis is an important zoonotic disease that affects animal and human health worldwide, particularly in developing countries including Pakistan. Punjab is the most populous province having about 50% of the human and animal population of the country. In Punjab, dense animal and human populations, smallholder farming systems, poor husbandry practices, and lack of vaccination and animal movement control are important factors that have increased the risk of transmission of the disease. Hence, the government has launched a brucellosis control program to reduce the spread of bovine brucellosis using an active surveillance approach. This large-scale population-based study under the brucellosis control program aimed to i) estimate the seroprevalence, ii) identify important risk factors and iii) provide the spatial distribution of bovine brucellosis in Punjab province of Pakistan. In this study, 12,406 livestock farms/holdings in 34 districts having 61,084 animals (31,307 cattle and 29,777 buffaloes) were georeferenced and screened for Brucella antibodies between July 2020 and June 2021 through an intensive network of laboratories under the directorate of Animal Disease Diagnostic, Reporting and Surveillance, Punjab. The sera were initially tested by Rose Bengal Plate Test and the positive samples were confirmed with Indirect Enzyme-linked Immunosorbent Assay. Data on various farm and animal-level risk factors was also collected and analyzed through multivariable mixed models. The overall animal-level seroprevalence was 0.84% (514/61,084), while the herd-level prevalence was 3.13% (389/12,406). Within-herd prevalence ranged from 0 to 100%. The prevalence was higher in the north-eastern part of the province with the highest in Hafizabad district [herd prevalence: 45.21% (95%CI: 39.88 – 50.64%), animal prevalence: 16.83% (95%CI: 14.49 – 19.38%)], followed by Gujranwala [herd prevalence: 22.17% (95%CI: 16.65 – 28.51%), animal prevalence: 7.47% (95%CI: 5.86 – 9.36%)], while in 14 districts no seropositive animal was detected. The spatial analysis identified significant clustering in the northeastern part of the province. The chances of Brucella infection were 1.70 times higher in females (P=0.029, 95% CI: 1.36-1.92) than in male animals. Every one-year increase in age increased the odds of being Brucella positive by 1.26 times (P <0.001, 95% CI: 1.22-1.30). The analysis of herd-level factors revealed that the odds of occurrence of brucellosis were 2.98 times on farms that had an abortion history (P<0.001). Similarly, farms with free stall housing (OR=3.01, P<0.001), animal purchase history during the last year (OR=1.35, P<0.001) and artificial insemination practice (OR=1.35, P<0.001) had also higher odds of occurrence of Brucella infection. In conclusion, the study provides a detailed insight into the epidemiology of bovine brucellosis in Punjab that will help to develop more targeted interventions for the control of brucellosis leading to better animal and human health
R10.4 Spatial distribution of poultry farms using point pattern modelling: a methodology to address disease transmission risks
No full textThe distribution of farm locations and sizes is paramount to characterize disease spread patterns. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource consuming.
Here, we develop a farm distribution model (FDM), which allows predicting locations and sizes of poultry farms in countries with scarce data. It combines (i) a Log-Gaussian Cox process model (LGCP) simulating the farm distribution as a spatial Poisson point process with logarithm varying intensity, conserving the level of clustering of spatial points patterns, and (ii) a random forest (RF) model simulating farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian province) and Thailand.
The LGCP and RF models yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on spatial analysis of the poultry production systems and spatial clustering drivers. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated vulnerability to epidemics. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the relevance of spatial clustering and farm sizes to study epidemic spread.
As the FDM maintains a realistic distribution of farms and their size, its use to inform mathematical models of disease transmission is very relevant for regions where these data are not available
P02.3 Being prepared for an avian influenza epidemic with a One Health approach: a cartographic study in Lazio Region to identify animal carcasses burial sites
No full textAccording to European and national legislation, in case of avian influenza outbreak or epidemics, carcasses of dead and culled animals should be sent to rendering plants (Ministry of Health, 2014). Competent authorities can authorize burial procedures, if rendering plant cannot dispose on time a large and unexpected number of carcasses (Chowdhury et al., 2019). The burial site should be identified on a specific site study and chosen in a suitable environment (Costa et al., 2019), it must ensure geological stability for several years and the confinement of organic material lasting from a minimum of 2 up to 20 years, to ensure the complete degradation of the organic material and pathogens. The aim of the present study was to create a mapping project to identify suitable sites for the burial of large volumes of avian carcasses in case of avian influenza epidemics. The territory of Lazio Region was classified based on the following factors: presence/absence of environmental constraints and regulations that protect the environment and limit the land use (landscape constraints, protected areas, archaeological sites, drinking water springs), features affecting the susceptibility to pollution (water table depth, hydrogeological vulnerability, hydrographic network) and stability over time (landslides, floods). Official geodata from National and Regional cartography were used: geoportal of the Institute of Environment Research and Protection (Istituto Superiore per la Ricerca e la Protezione Ambientale - ISPRA), geoportal of the Ministry of the Environment (Ministero dell’Ambiente e della Sicurezza energetica), geoportal of Lazio Region (Open Data Lazio), Lazio Region Environment Information System (Sistema Informativo Regionale Ambientale - Sira); geodata archive of the Istituto Zooprofilattico Sperimentale del Lazio e della Toscana (IZSLT). The output is an interactive map that allows to navigate and to choose which different layers apply among the existing 19 layers. The map is available through an ad hoc created website or the exchange of shapefiles/layers of proprietary or open-source geographic information systems. The intended users are regional authorities, Municipalities, veterinarians of Local Health Units, poultry breeders and other stakeholders. The map allows to identify areas not suitable for animal carcasses burial because subject to constraints or environmental risks. Areas identified as suitable should be further investigated at local level by core samples, soil inspections, existing land-use plans or other resulting constraints not present in this study. In conclusion, overlaying different geographic information levels identified potential areas of suitability for animal carcasses burial in case of infectious diseases epidemics. The use of suitability maps collaborates in preventing impacts on environment, health and animal/human interface
R05.2 Use of Satellite Earth Observation to monitor aquaculture sites in coastal Abruzzo region, Adriatic Sea
No full textGenerally, river plume waters can be distinguished from seawater by differences in salinity, temperature, turbidity, suspended matter and dissolved organics. Optically active water constituents interact with light, and their reflectance can be measured using Satellite Earth Observation (SEO) (Lombardi et al., 2022). When getting close to coastal zones, the effects of anthropogenic and natural activities on the sea require a finer observation scale than ready-to-use SEO-derived products. To get mapping products time series at high spatial resolution from SEO, local calibration of retrieval algorithms with in situ data is required to accurately estimate concentrations of near-surface parameters, although the collection of dedicated in-situ data is not easily available.In this study, we calibrated regional algorithms based on Copernicus Sentinel-2 MultiSpectral Instrument (MSI) data with the value domain typical of the coastal area of central Adriatic Sea to build accurate estimates of turbidity and chlorophyll-a parameters. The algorithm 'C2RCC' (C2X-Nets) (Brockmann et al., 2016) available in the Water Color Data Analysis System (WC-DAS) (Filipponi et al., 2021) was regionally (Abruzzo coast) calibrated with ad hoc collected data during 12 boat campaigns (years 2019-2020), in 20 sampling points distributed between Pescara river mouth and a mussel farm. We estimated and analysed mapping products time series at 10 m spatial resolution, related to turbidity (in FNU) and chlorophyll-a concentration (in mg/m3), from all available Sentinel-2 MSI satellite acquisitions in the period 01 July 2016 - 31 December 2021 (total of 589 observations). On average, in situ data turbidity decreases and salinity increases when moving away from the coast. This general trend, expected when the softer and colder waters of the river mix with the saltier and warmer marine waters, is well captured by SEO imagery: turbidity values in the coastal waters (0-3 nautical miles NM) of Abruzzo region has mean value 4.48 FNU ± 1.58 standard deviation.Chlorophyll-a mean value is 0.20 ± 0.04 standard deviation in 0-3 NM coastal waters of Abruzzo region, indicating oligotrophic waters. There are many potential benefits of using SEO to support sea and public health as well as economic activities. For aquaculture purposes, SEO data provides (i) mapping of parameters at high spatio-temporal resolution to (ii) more accurately monitor environmental and sanitary conditions. Spatial analysis of SEO data helps in (iii) assessing the most suitable areas for aquaculture farming and can underlie Marine Spatial Planning. Furthermore, in combination with weather forecast, SEO data facilitates (iv) modelling in active forecasting systems and alert to timely intervene to safe production and mollusc quality
K08 Emergent infection disease problems under environmental land management
No full textThe world is experiencing environmental change which is unprecedented in its speed, geographical reach and severity. At the same time, biodiversity loss is in many ecosystems near catastrophic and food security a growing worldwide concern. Both long terms trends and individual system shocks have already been shown to have far-reaching implications for global health. In response, actions over land management to respond to those threats must be equally drastic and our decisions in the short term will have far-reaching implications for decades to come. These conditions are also already known to influence the emergence of new infectious disease threats. In turn, infectious diseases have the capacity to derail our efforts to respond to these challenges. Here, I shall discuss some of the issues involved, and present some preliminary investigations into the impact of how spatial coordination of land management decisions and infectious disease risks impact the potential for infectious diseases to affect wildlife and livestock