Veterinaria Italiana (Journal)
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K1 The ever expanding Brucella genus
No full textPapers on Brucella and brucellosis often begin with ‘Brucellosis is one of the world’s most common zoonotic diseases’. However, this just refers to Brucella melitensis, B. abortus and certain B. suis biovars. These are the most commonly isolated Brucella strains because they have been spread across the world by the livestock industry over past centuries. They are also the most common causes of human brucellosis mainly because they are the species that humans are most exposed to.The Brucella genus is much larger than these three species and, in the last 25 years, has rapidly expanded from 6 to 12 recognised species with several more strains not yet assigned officially. The new strains have been isolated from humans, diverse mammals and, more recently, fish, amphibians and reptiles. It is most likely that many more new strains will be identified in the future.Whole genome sequencing has shown that the genus can be divided into two distinct clusters; the ‘classical’ Brucella, including the major zoonotic strains and the ‘atypical’ Brucella containing many of the new isolates. Many of the ‘atypical’ strains are phenotypically distinct form the ‘classical’ strains showing rapid growth, motility, a chemically and antigenically distinct O-antigen and a unique metabolic capacities. These atypical strains highlight the evolutionary path of Brucella from a soil bacterium. Very recently, the taxonomy of the genus Brucella has been complicated by the reclassification of other members of the Brucellaceae to the genus. These bacteria are soil bacteria or endophytes associated with different plants that do not cause a brucellosis like disease. This change is greatly contested by the Brucella research community.Due to their importance to the livestock industry and to human health, most studies concerning the virulence and zoonotic potential of Brucella have been with on B. melitensis, B. abortus and B. suis. In this Keynote Lecture, I will give an overview of how the Brucella genus has expanded and discuss to what extent the new strains represent a threat for animal or human health. I will also comment on the problems associated with the inclusion of non-Brucella strains in the genus.
Contact author: [email protected]
K4 Internal affairs: defining how cytosolic receptors sense Brucella and contributes to host defense
No full textBrucella has developed a stealth strategy through pathogen-associated molecular patterns reduction, modification, and hiding to ensure low stimulatory activity. This strategy allows Brucella to reach its replication niche before activating antimicrobial mechanisms by host immune responses. However, inside the host cells, Brucella releases vital molecules for the bacteria that trigger the activation of host cytosolic receptors. First, we defined that Brucella LPS is the ligand for the receptor caspase-11. Additionally, we determined that B. abortus is able to trigger pyroptosis leading to pore formation and cell death, and this process is dependent on caspase-11 and gasdermin-D (GSDMD). Mice lacking either caspase-11 or GSDMD were significantly more susceptible to infection with B. abortus than wild-type animals. Our findings suggest that caspase-11/GSDMD-dependent pyroptosis triggered by B. abortus is important to infection restriction in vivo and contributes to immune cell recruitment and activation. Besides LPS, DNA is another important bacterial ligand. Then, we determined that the cGAS/STING pathway is able to recognize bacterial genomic DNA and cyclic dinucleotides. Further, we have demonstrated that STING but not cGAS is critical for host protection against Brucella infection in macrophages and in vivo. Additionally, we revealed that STING contributes to an inflammatory M1-like macrophage profile upon Brucella abortus infection. This metabolic reprogramming is induced by STING-dependent stabilization of hypoxia-inducible factor-1 alpha (HIF-1a). HIF-1a stabilization reduces oxidative phosphorylation and increases glycolysis during infection with B. abortus and enhances nitric oxide production, inflammasome activation and IL-1b release in macrophages that are involved in reduced bacterial replication. In summary, identifying innate immune receptors and their ligands is critical to the development of new vaccines and control measures against Brucella infection. In addition, we speculate on the prospect of targeting immunometabolism in the effort to develop novel therapeutics to treat brucellosis and other bacterial infections
P02.1 Geospatial Data to Support Veterinary Avian Flu Surveillance in the Autonomous Province of Bolzano (Italy)
No full textThe domestic fowl population in the Province of Bolzano, 520,000 inhabitants, northern Italy (Alps), currently amounts to approx. 156,700 heads (domestic poultry, turkeys and ducks, i.e. Gallus gallus, Meleagris gallopavo, Anas platyrhynchos domesticus) at a total 207 farms, very few of which hold over 5000 birds. Currently, the province is not a high-risk area for Avian Influenza (EFSA et al. 2023). Although major migratory routes cross the province, there are few wetlands serving as wintering sites for wild bird species. However, there are small rural poultry farms for private consumption throughout the province and, albeit at low density, several commercial farms containing laying hens, broilers, turkeys and species of lesser economic importance. On average, these farms, also due to consumer attention to animal welfare, offer outdoor access. Free-ranging birds leaving the coop increase the risk of Avian Influenza infection through contact with wild fowl. In addition, the need for commercial farms to source poultry flocks exposes them to diseases introduced from other regions or European countries with higher infection risks.
Given the need to implement the government’s annual Avian Influenza surveillance plan and the decrees concerning biosecurity at poultry farms, it is essential to monitor Veterinary Public Health interventions and to practise outbreak preparedness.
Until present, no Avian Influenza cases have been reported in the province; however, cases are being reported in all neighbouring provinces (IZS 2023, July 12). Between 24/02 and 02/03/2023, three cases of H5N1 in Black-headed Gulls (Chroicocephalus ridibundus) only 50 km from the provincial borders further substantiated the risk of an Avian Influenza introduction (IZS 2023, July 13).
The project aimed to provide local veterinary services with a tool for more effective, close monitoring of disease emergence.
GIS data of all registered poultry farms were collected with details at municipality level, adding geospatial data of sites with the largest waterfowl concentrations and linking them to information about each farm (bred species, number of heads) using ArcGIS Desktop (Version 10.5). The farm coordinates were collected alphanumerically and imported to the map as a point layer. Lakes and biotopes were georeferenced using orthophotos from 2020. The river line layer was delimited by an initial and an end point, which were indicated. The coordinate system used is ETRS89 UTM zone 32N (EPSG: 25832).
Five different geographic distribution maps were prepared for the entire territory and distributed to the veterinary services of the four health districts. An information seminar was organized to inform both veterinarians and poultry breeders. At present, browsable online versions are being explored.
With respect to emergency preparedness, response and management regarding Avian Influenza, geographic information systems should be considered an important additional tool for the Veterinary Public Health sector and routinely used in One Health strategies
R08.1 Multihost foot-and-mouth disease dissemination model: why using just cattle data neglects the disease spread potential and covered transmission routes
No full textFoot-and-mouth disease (FMD) infects multiple food-animal species and disseminates among ungulate species. FMD is known to disseminate through a combination of within-host and between-host dynamics. Several single-species FMD transmission models have been used to simulate outbreaks and examine the effects of control actions to mitigate outbreaks, however, often focusing on individual food animal species such as cattle. Our multi-species and multiscale (metapopulation and between-farm dissemination driven by animal movement and farm-to-farm distance) compartmental stochastic model accounts for births, deaths, and species-specific transmission dynamics. Our model outputs included the number of secondarily infected animals and farms, the role of animal movement and between-farm distances as transmission pathways, and the effectiveness of countermeasure actions. Our results demonstrated that after 20 days of FMD dissemination without any control actions, all the species were infected, and the median number of infected farms was eight. The spatial proximity was the predominant route associated with bovine infection, while in swine, it was linked with animal movements.
Furthermore, the median distance between seeded, and secondary infections was 5.77 km, with the highest spatial dissemination reaching 695.40 km. The simulated control strategy results showed that depopulating 12 farms and vaccinating 15,000 farms daily after 20 days of silent FMD dissemination would contain 93.4% of epidemics, with a median of, nine infected farms within 54 days after implementing control actions. In conclusion, our model highlights the need for multispecies FMD transmission models, especially in regions where multiple species are raised on the same premises
S02 Mapping global coldspots of veterinary capacity
No full textMaps have been instrumental in prioritizing interventions against infectious diseases of global importance (Reich & Haran, 2018). In human medicine, high-resolution maps have helped to quantify the burden of diseases such as malaria and dengue. Similarly, in animal health, mapping efforts have focused on diseases such as Avian Influenza, Rift Valley Fever, or antimicrobial resistance, which pose threats to the livelihoods of those who raise animals for subsistence.
However, mapping the accessibility of veterinary care has received relatively little attention thus far. Veterinarians are at the forefront of defense against animal and zoonotic diseases (Bellemain, 2013). Yet, efforts to map the distribution of veterinarians have primarily concentrated on regional or provincial levels within a limited number of countries. Although crucially important, these efforts can overshadow important heterogeneities in access to veterinary care in countries currently transitioning from subsistence farming to commercial farming, which have also been suggested to be the most vulnerable to disease emergence.
In the past decade, online platforms enabled users to locate veterinarians based on postal codes or addresses (Royal College of Veterinary Surgeons, 2023). The purpose of these platforms is to match patients with veterinarians working in their vicinity. Whilst not primarily intended for public health, these platforms are an unprecedented opportunity to get insights into the fine-scale distribution of veterinarians, the factors driving that distribution, and supplement existing national-level data sources on veterinary capacity.
In this study, we predicted and mapped the global distribution of veterinarians at the 10x10 km2 resolution using a global address book of veterinarians assembled from online sources. Specifically, we used web-scraping techniques and queried online web maps to collect the locations of 303,745 veterinarians across 115 countries. These locations were modeled through a Log-Gaussian Poisson Regression model implemented in a Bayesian framework provided by the Integrated Nested Laplace Approximation (INLA). By comparing the distribution map of veterinarians with density maps of animals raised for food we mapped regions where animals are more than 1 hour away from veterinarians. The 91.3% of these regions, referred to as “veterinary coldspots”, were found in low- and middle-income countries. Finally, we furtherly identified, for 103 countries, the geographic locations where veterinary services should be scaled up to maximize access to care for animals raised for food
R04.2 Combining key hazard- and exposure-related drivers to model the probability of occurrence of TBE human cases in Europe
No full textIn the last decade, the number of Tick-borne Encephalitis (TBE) human cases reported in Europe has increased both in endemic and in non-endemic areas (European Centre for Disease Prevention and Control, 2022). The geographical occurrence of TBEv is fragmented with “foci of infection” (“hotspots”) that are difficult to identify and often vary in space and time (Dobler et al., 2011). To improve the capability to identify the European regions at high risk of outbreaks, we developed a spatio-temporal predictive model inferring the year-to-year probability of occurrence of TBE human cases in Europe.
We used data provided by the European Surveillance System (TESSy, ECDC) to infer the distribution of TBE human cases at the regional (NUTS3) level during the period 2017-2021. We included variables related to temperature, precipitation, land cover and ticks’ hosts presence to account for the natural hazard of viral circulation. We also used indexes based on recorded intensities of human outdoor activity in forests as proxies of human exposure to tick bites. We identified the yearly probability of TBE occurrence using a boosted regression tree modeling framework.
Areas with higher probability for transmission were identified in Central-Eastern Europe and along the coastline of Nordic countries up to the Bothnian Bay. Our results highlighted a westbound and northbound spread of TBE-positive regions throughout the years. Areas at higher risks are characterized by the occurrence of key rodent reservoir and cervid species, intense human recreational activities in forests, steep drops in late summer temperatures and high annual precipitation amounts. The predictive accuracy of the model was assessed through internal and external validation (AUC = 0.84; CBI =0.98).
Our study provides an assessment of the European regions at risk of TBE human infections on a yearly basis. Our results can therefore be used to evaluate the yearly risk of occurrence of TBE human infections, at different spatial scales, and to support surveillance and prevention campaigns within endemic and potential new risk areas
R05.1 How to integrate satellite-derived indicators into models of animal mobility?
No full textIn a context of increasing anthropization at the interface between protected areas and rural communal areas in southern Africa, the multiplication of interactions between wildlife and livestock may generate conflicts such as competition for natural resources, predation, crop destruction by wildlife, or the risk of pathogen transmission. To better understand these potential contacts, we have developed a method combining remote sensing and spatial modelling to simulate the movements of domestic ruminants (cattle) and wild animals (buffaloes).
Water surfaces and vegetation, identified as the main drivers of movement for these ungulate species (Rumiano et al., 2020), were derived from a series of Sentinel-2 satellite images. The resulting maps were then integrated into a mechanistic model of collective movement of interacting individuals (Gregoire et al., 2003), applied to buffaloes (Rumiano et al., 2021) and cattle. The model, tested at three study sites in Zimbabwe, simulates herd movements, the location of contact zones and their seasonal dynamics.
Model results were compared with GPS collar location data from 34 individuals (16 buffalo and 18 cattle). The results show a high degree of spatial and seasonal variability in buffalo-cattle contacts in the three study areas, and a correspondence at landscape scale between the spatial extensions of modeled and observed contact zones. These results illustrate the potential of spatial modelling combined with remote sensing to simulate animal movements on a landscape scale, while offering possibilities for managing these interfaces through, for example, a coupling with epidemiological modelling, or the testing of different scenarios of changes (e.g., practices, environment, climate)
R02.1 Anthropogenic and environmental factors associated with koala deaths occurring through vehicle collisions and dog attacks in South East Queensland, Australia
No full textPopulations of the iconic Australian koala are under constant decline. Their deaths are associated with the rapid and extensive urbanisation and fragmentation of koala habitat. Using citizen science data on observed koala mortalities in South East Queensland, we quantified the association between environmental, infrastructural and demographic factors and the two leading causes of koala deaths, vehicle collisions and dog attacks. We achieved this objective using two modelling approaches: a Poisson point process model to identify factors increasing the intensity of a given cause-specific mortality and a case-control design to estimates the odds of a given cause of death compared to all other causes of death.
The intensity of koala deaths (that is, the expected number of koala deaths per square kilometre) from vehicle collisions was negatively associated with distance to primary roads, whereas the intensity of koala deaths from dog attacks was positively associated with dog population density. While these results were expected, the value in these analyses arose from the ability to identify specific areas where there was an excess of cause-specific mortality risk after known risk factors had been accounted for.
The results of this research can be used to develop risk mitigation strategies and to enhance surveillance for dog attacks in high-risk areas, for example by conducting educational awareness programs, promoting registration of dogs and discouraging free roaming of dogs. In a similar manner, in high-risk areas for vehicular collisions, safe over or underpasses can be built to facilitate safe movement of koalas for road crossing or speed limits could be introduced to reduce the likelihood of koala deaths
P10.7 Spatial dynamics of mallard ducks (Anas platyrhynchos) and their potential role in the spread of Avian Influenza in Italy
No full textBetween January 2017 and March 2018, Italy experienced a severe H5N8 Highly Pathogenic Avian Influenza epidemic, which was characterized by two waves: the first occurring from January to May 2017, and the second starting in July 2017. Interestingly, the second wave coincided with a period when migratory waterfowl were scarce or absent, indicating the potential involvement of resident wild bird populations. This led to the hypothesis that the virus might have amplified in nestlings and juvenile residential birds in the period between the two waves, and subsequently spread in July and August, when the juveniles left the natal areas following the moulting period.
A study was conducted between July 2019 and May 2021, to assess the extent of the movements of mallard ducks (Anas platyrhynchos) in their different phenological periods: breeding (May-June), moulting (July), post-moulting (August-October), and wintering/resting periods (between November and the following February). The study involved the placement of GPS-GSM transmitters on 73 mallards (37 males and 36 females) captured in two coastal and two inland wetlands, in proximity to the Densely Populated Poultry Areas in northeastern Italy. The GPS and satellite-derived data were used to assess the home ranges and land-use in the phenological periods. The results were then used to compute a measure of the probability of finding mallards within the maximum extent of their movements and, consequently, estimating the risk of contact with poultry farms.
The home ranges of mallards and their land-use patterns exhibited marked differences throughout the four periods and accordingly to sex and capture area. During the moulting period, movements resulted very limited, remaining confined within the wetlands of capture and their immediate vicinity. However, during the post-moulting and wintering periods, birds exhibited longer movements, especially those captured in inland wetlands. In the post-moulting/wintering periods the coastal wetlands showed frequent exchanges of birds, forming a territorial continuum. The coastal birds predominantly utilized natural wetlands, with occasional incursions into agricultural areas, while the inland mallards resulted more likely to visit agricultural settings and, to a much lesser extent, urban environments. The probability of encountering mallards was higher in the post-moulting and wintering periods, and for inland birds. For the coastal mallards, the highest presence of birds was confined within the wetlands, with minimal likelihood of encountering birds in the inland areas.
The study of mallard movements provided insights into their behaviour during different phases of their life cycle, highlighting variations in home range and land-use patterns. The measurement of the likelihood of finding mallards accordingly to the land-use might be used in developing risk-based surveillance plans for avian influenza in both wild birds and domestic poultry, aiming at focusing efforts in well-defined areas and temporal periods
R01.3 Precision epidemiology in practice: applications to better prevent and control endemic diseases in the US swine industry
No full textSimilar to precision medicine or precision public health, precision veterinary epidemiology uses high-resolution data (from genome to phenome and beyond) to get further insights of the epidemiological problem and support the timely selection of customized interventions to specific groups of animals, farms or productions systems that are more adequate to address their specific needs. This approach requires Big Data that are not only genetic (both at host and agent level), but also include animal and environmental characteristics, management practices, biosecurity measures and animal movements at a farm and production system level.
This precision epidemiology concept is based on the novel development, application and operational accessibility of advanced data mining and machine learning approaches that effectively evaluate the complexity and multi-level nature of the epidemiological problem and that, in combination with decision support platforms, facilitates the timely analysis, visualization, communication and sharing of information to support decisions at population level and achieve global herd/farm/system optimization.
In this work we illustrate how we are using precision epidemiology in the swine industry using the Disease BioPortal platform to empower veterinarians and farmers through advanced data-driven analytics & customized dashboards to more proactively manage animal health. Our approach combines multiple analytical tools such as geographic information systems, phylogenetics, network analysis and machine learning models to dynamically analyze the distribution of disease over space and time and create predictive models adapted to epidemiologically changing scenarios. But finally, this complex process needs to be translated in clear and simple visualizations of results or risks which can be easily interpreted by the final users (i.e. field veterinarians, production managers, diagnosticians, etc.). We will show how users can evaluate risks for animal collectives and support real-time responses in changing scenarios, which may include, for example, from the emergence of an outbreak in an area to changes on vaccination strategies or commercial policies.
Although we believe precision epidemiology will vertically contribute to the overall animal health, animal welfare, farm productivity/sustainability and revenue in the livestock industry; its application is in its infancy and it still requires critical advancement and substantial changes in the way we collect, standardize, integrate, share and use data in veterinary medicine. We share our experience addressing some of these challenges and provide some recommendations and future directions