Veterinaria Italiana (Journal)
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K2 Next-generation proteomics in the fight against pathogens: a guide tour
No full textSince proteins are the workhorses of biological systems, their global study by proteomics provides a wealth of information. Next-generation proteomics is based on high-resolution tandem mass spectrometry which can record spectra on hundreds of thousands of different molecules, allowing the identification and quantitation of thousands of proteins. Highly complementary to genomics and transcriptomics, proteomics better explain the resulting phenotype and can be performed in a timely manner. Interestingly, it can be applied to any pathogen, delivering a better understanding of its specific characteristics as well as new insights into the host response to infection. Remarkably, the identification of virulence and antibiotic-resistance factors, as well as other key molecular players, became ultimately straightforward thanks tocomparative proteomics. Pathogen detection by proteotyping is also a promising methodology that takes into account taxon-specific information that can be rapidly recorded by mass spectrometry and interpreted for rapid diagnosis. Indeed, tandem mass spectrometry is able to quickly distinguish closely related strains and helps in their taxonomical study. Finally, pathogens are often part of more complex microbial communities. Recent advances in metaproteomics make it possible to address pathogens in such a complex environment. Challenging questions in metaproteomics benefit from recent advances in bioinformatics and this methodology has gained maturity for envisioning now its large scale application in clinical settings. The different facets of proteomics and metaproteomics, including sample preparation, data acquisition, database construction, search strategy and dat interpretation, will be presented and discussed in light ofseveral studies carried out on a variety of pathogens and biological questions
K8 Control of Brucellosis in Domestic Ruminants. Yes we can!
No full textThe incidence of human brucellosis is persistently high and even increasing. However, the situation of the main reservoir of this zoonosis with major health and economic consequences, namely the domestic ruminant population, remains largely unknown. Indeed, apart from reports on the situation in few regions where sound and effective programmes are in place, and a few targeted studies on limited areas in variouscountries, there is currently no clear picture of the epidemiological situation and the control programmes in place around the world. It is surprising, in this respect, that presentations on this subject at international congresses are rare, as this congress again demonstrates. Clearly, some knowledge or tools still need to be developed or refined. However, the experience gained in several regions, notably Europe and North America, and the research carried out over many decades show that effective tools are available and the appropriate methodology sufficiently advanced to make substantial progress in the control or even eradication of this reservoir. Resource constraints, both human and financial, but also and above all a lack of political will to conduct long-term control programmes in close association with the concerned stakeholders, are obviously to blame. This presentation, which opens the session on ‘Epidemiology, control and eradication’, aims to reiterate the main elements of a diagnostic, health decision and control strategy based on the knowledge acquired in the epidemiology of the disease and on the basic principles of infectious disease diagnosis in general. It is hoped that, at a time when the One Health concept is on everyone’s lips, the animal and public health authorities will become more actively engaged in the fight against this infection, which is so detrimental to both human and animal populations
R05.4 Tick's suitability habitat maps and tick-host relationships in wildlife. A One Health approach based on multitemporal remote sensed data, entropy and Meta® population dataset in Aosta Valley, NW Italy
No full textTicks represent a reservoir of zoonotic pathogens, and their numbers are increasing largely in wildlife. The development of Earth Observation (EO) missions and GIS-Remote sensing techniques have contributed to the enhancement of epidemiological data analysis capabilities providing new and potential tools for the surveillance of animal diseases (Estrada-Peña, 2002). Moreover, the correctly geomatic-based use and development of remote sensing and EO Data in veterinarian ordinary workflows is still under-exploited.
In this regard, to strengthen the use of free EO data in ordinary veterinarian analysis, this work aimed identifying the tick species most involved, their distribution in wild hosts and develop suitability habitat maps (SHM) in Aosta Valley (NW Italy) based on multitemporal EO data processed in Google Earth Engine providing also a risk assessment (Orusa et al., 2023). SHM were realized considering the following inputs: A) Growing Degree Ticks (GDT) computed from Land Surface Temperature from MOD11A1 by assigning a threshold, B) NDVI from MOD09GA involving the pixels satisfying the GDT threshold, C) NDVI entropy, D) water surfaces distance, E) terrain features, F) Precipitation retrieved from WORLDCLIM/V1/BIO.
Clouds, shadows and defective pixels have been masked out in the EO data considered and monthly composites within the years 2020-2022 created. Since the data of the ticks have not been punctually geo-referenced, not allowing a multivariate regression analysis of the variables involved in the suitability map, to define the weight of each one, on the basis of the existing literature, the variables considered have been assigned the same weight and have been normalized. An ISODATA unsupervised classification–clustering algorithm was performed on B), and the separability of each class obtained was checked by computing the Jeffries–Matusita distances to compute than C).
Ticks were collected from hunted, injured, and found dead wild animals (Sus scrofa, Capreolus capreolus, Rupicapra rupicapra, Cervus elaphus), and they were identified to species level using taxonomic keys (Accorsi et al., 2022). Between September 2020 and December 2022, a total of 90 ticks were collected from 89 wild animals. Ixodes ricinus (93.4%) was the most prelevant tick species, followed by Dermacentor marginatus (5.5%) and Dermacentor spp (1.1%). In roe deer, Ixodes ricinus was the most frequent species founded, as Dermacentor spp., while in wild boar Dermacentor marginatus was the dominant species. Molecular analyses demonstrated the infection of ticks with Anaplasma spp., B. burgdorferi sensu lato, Coxiella burnetii, Rickettsia spp.
Finally, to assess population potential exposure to tick presence during the period investigated, the Meta® population dataset was considered by performing zonal statistics per each suitability class.
In conclusion this study shows the potentialities of Remote sensing and how the use of different information layers can allow a technological transfer to the veterinary sector according to a real One Health perspective
R03.2 Reducing data collection costs through sensitivity analysis of spatial data in GLEAM
No full textThe United Nations Food and Agriculture Organization (FAO) has developed the Global Livestock Environmental Model (GLEAM), a spatial data framework for estimating greenhouse gas (GHG) emissions from the livestock sector. In addition to livestock distribution data, GLEAM incorporates five spatially distributed variables associated with nitrogen and methane conversion factors. These variables include maximum methane production capacity from manure, climate zones, temperature, and losses due to leaching from solid and liquid manure management. All based on the 2019 and 2006 guidelines from the Intergovernmental Panel on Climate Change (IPCC). GLEAM has been used by several countries to estimate the impact of GHG mitigation actions and report these as part of their Nationally Determined Contributions. This process necessitates the development of a country-level data collection plan, which requires significant financial and planning resources. This study examines the impact of variability in spatial parameters on direct emission estimates by developing a sensitivity index at global and country scales. The results highlight the value of incorporating sensitivity analysis when designing data collection efforts, to reduce associated costs
R07.2 Predicting spatially explicit swine premises using deep learning and aerial imagery to improve disease monitoring and surveillance
No full textAccurate farm geolocations are essential for disease outbreak surveillance and monitoring, and for developing spatial epidemiological models that evaluate distance-dependent transmission processes across different farm types. Farm geolocation data is often not available, which leads to assumptions of the spatial distribution of livestock premises; potentially resulting in over/underestimation of transmission rates, misleading model predictions, and limited interpretation of epidemic dissemination dynamics (Sellman et al., 2020). Here, we developed a convolutional neural network (CNN) model to i) identify individual swine barns from non-barn features (e.g., residential homes, commercial buildings), with the objective of ii) developing a rule-based filtering method to calculate the area and the aspect ratio of swine barns and combine this information with farm metadata (pig capacity, farm type) to iii) predict farm types and population size.
High resolution aerial imagery from a 22,369 x 13,457 pixel extent was obtained from the National Agriculture Imagery Program (NAIP) (USGS, 2022), and used to generate 1,148 raster tiles with dimensions of 512 x 512 pixels. We constructed a binary mask consisting of 118 known swine barns, which we incorporated into the raster tiles as an additional raster band. Raster tiles were split as 60% for training and 20% for validation, and testing a ResUnet CNN model (Diakogiannis et al., 2020), respectively. Model performance was evaluated by examining the accuracy of the classification of barn and non-barn features.
The CNN model was able to accurately identify swine barns from non-barn features with an accuracy of 80% in a swine production region of the United States. Additionally, the fractional predicted probability of the “barns” class, recognized open space between barns and nearby waste lagoons as a high probability of belonging to a swine barns.
We were able to successfully predict swine barn geolocations using aerial images and deep learning with an accuracy of 80%. We also obtained valuable contextual information regarding environmental, demographic, and structural properties of swine barns and the surrounding areas, which we will be incorporating as we continue to refine our model. Once the best model is identified, our aim is to use this model to predict different swine farm types, and their corresponding population size. Results obtained from this work will enable researchers and policymakers to better understand the distribution and spatial arrangement of swine farms, and aid in the development of accurate spatial epidemiological models. This information is crucial for the identification of high-risk areas, potential sources of disease transmission, tailoring disease control strategies, and evaluating their effectiveness
P06.5 Introduction and exposure of wild boar to African swine fever virus by contaminated pork products in Spain via seaports
No full textAfrican swine fever (ASF) is a severe hemorrhagic disease that affects domestic and wild suids. Since its introduction nine years ago in the European Union, the wild boar population is one of the main drivers of the disease's local spread and its maintenance there. Spain is currently free of ASF, although the risk of introduction is non-negligible. Due to the worldwide distribution of the disease, contaminated pork products with ASF virus (ASFV) pose a major threat to ASF-free regions. Thus, the aim of the study was to identify areas in mainland Spain with a higher risk of indirect exposure of wild boar to ASFV-contaminated pork products illegally introduced via seaports in the event that the scenario ever occurs.
First, the Spanish seaports that receive traffic from ASF-affected countries were detected. A risk score was assigned to each seaport based on several parameters, related to the intensity of connections between each at-risk seaport and the origin countries, the number of connections, as well as the level of ASF affectation of the origin countries. Places where the indirect exposure of wild boar to potential ASFV-contaminated products was identified: gas stations, rest areas, and near roads in protected natural spaces. Then, we used a weighted linear combination analysis accounting for the Kernel density of the inverse distance of gas stations, rest areas, and protected natural spaces to seaports, as well as the first two contact areas to the suitability of wild boars.
Our findings highlighted relatively small at-risk areas where wild boar exposure to ASFV-contaminated products could occur. Two seaports, namely Barcelona and Valencia, presented a higher relative risk of wild boar exposure and higher risk score. This early warning system tool can aid with the implementation of cost-effective active surveillance and preventive measures in the framework of the Spanish wildlife health surveillance program for ASF
P04.10 Data and Other Considerations When Using GIS to Support Animal Emergency Preparedness and Response in Appalachia
No full textThe Appalachian Region in Eastern United States is a socioeconomically challenged area that spans 206,000 square miles across 13 states. Over 26 million people live in the 423 counties and 8 independent cities included in this region. The Appalachian Region is often classified as ‘rural’, but the United States Department of Agriculture (USDA) Economic Research Service only classifies about 25% of the localities as truly rural with the others falling along the urban spectrum. This indicates that the Appalachian Region is not homogeneous in nature and should not be treated as such when it comes to planning for animal emergencies.
Animal population estimates for this project were obtained from multiple sources. Livestock and poultry numbers were downloaded from the USDA 2017 Census of Agriculture. County-level dog and feline numbers were calculated based on United States Census Bureau household numbers and American Veterinary Medical Association state level multipliers. State veterinary licensure data for the 13 Appalachian states was aggregated at county level for those who listed an in-state address only. AVMA veterinary clinic data was accessed through ESRI ArcGIS Online. Other data layers, ecoregions and internet distribution, were also added to the project in ESRI ArcMap 10.8.2.
Cattle, equine, and small ruminants are distributed throughout most of Appalachia with an exception being some of the counties located within the Appalachian Mountain ecoregion. Swine and poultry are heavily concentrated in the southern part of Appalachia with pockets found in several other areas. The distribution of dogs and cats align with the human population distribution since the calculations involve household numbers. More than 27% of the localities in Appalachia either have ‘0’ or ‘1’ veterinary clinic located in their vicinity. Finally, multiple jurisdictions report less than 70% of households have internet subscription which doesn’t necessarily reflect access to broadband internet.
While planning is usually done locally, a regional exploration of data might be helpful in locating other jurisdictions that might have a similar distribution of animals enabling localities to share best practice or plans. For instance, 45 counties report more than 2.5 million broilers. Additionally, a regional approach might be needed if an area is a ‘veterinary desert’ and there are not many options to house or treat animals in certain situations. One issue with large datasets is the accuracy of the data. For instance, another equine population estimate reports that are over twice the total number of equine in the US compared to the USDA data meaning methodology and limitations must be explored to determine which number to include as this could affect the ability and capacity to respond to animal emergencies. Finally, GIS can be used to identify impediments such as geography or inability to communicate which may affect planning
P09.3 Genomic surveillance and phylogeographic analysis of vampire bat rabies across Central America using FTA cards and Nanopore sequencing
No full textVampire bat rabies (VBR) poses a major threat to animal and human health and is a chronic economic burden across Central America. Current efforts to reduce the burden, including vaccinating humans and livestock, and bat population control, would benefit from deeper understanding of viral circulation in the bat reservoir. However, long term viral maintenance of VBR arises through complex spatial processes, including co-circulation of viral lineages and transboundary viral invasions. We hypothesise that the highly heterogeneous, narrow, landscape of Central America could favour viral diffusion along ‘transmission corridors’, which may be strategic points to target interventions. To understand epidemiological linkages within and across countries, international scale molecular level monitoring is required, but existing national surveillance systems are largely disjoined and most lack capacity for virus molecular characterisation.
We present viral genomic data from 7 countries derived from a new cooperation involving the 9 member countries (Belize, Costa Rica, Dominican Republic, El Salvador, Guatemala, Honduras, Mexico, Nicaragua and Panama) of the International Regional Organisation for Agricultural Health (OIRSA). Rabies positive brain samples are preserved in duplicate on Whatman or Qiagen FTA cards for robust sample banking in the country of origin, and room temperature shipping to regional reference laboratories. Rabies genomes are enriched by amplification of 400bp overlapping regions, prior to WGS using Oxford Nanopore Technologies MinION. Regionally derived sequences and spatiotemporal metadata are analysed using IQTREE for phylogenetic tree generation and MADDOG for high-resolution lineage assignment with results updated on a map for internal reporting.
Initially, 126 FTA cards were obtained from 7 countries from wild and domestic animals, spanning 8 years. Whole genome sequencing (WGS) coverage of >90% was obtained for 65% of the first 58 samples processed using two initial primer schemes (broadly representing ‘North’ and ‘South’ viral circulation). We found strong amplification bias towards lineages most closely related with the reference sequences, but only 7% of samples were undetectable. Our analysis designated 3 new rabies lineages, for which we observed within-country co-circulation, and revealed high levels of viral sharing between neighbouring countries according to the clustering of the phylogenetic tree.
Our regional rabies sequencing network has built laboratory and data analysis capacity in a region where few full rabies genomes were previously available. Future work will apply evolutionary landscape resistance models to our growing VBR dataset to reveal the spatial-temporal dynamics of VBR dispersal and transmission corridors, ultimately supporting more efficient rabies prevention strategies
R10.7 A raster-based compartmental model combining the host's density and movements, driven by vector suitability
No full textVector-borne diseases cause significant veterinary and public health burdens as they widely re-emerge in affected areas or emerge in new healthy areas. Understanding, predicting, and mitigating the spread of vector-borne disease in diverse hosts and geographic areas are modeling objectives still to be explored. Many scientific studies are focused on developing predictive models for vector distribution and suitability to identify high-risk transmission areas and plan disease prevention and control strategies (Nguyen et. al., 2023; Saucedo & Tien, 2022; Leta et al., 2019).
Vector distribution is driven by climatic, geographic, and environmental factors (such as temperature, precipitation, and altitude), and understanding its presence and abundance is fundamental to planning effective prevention and control strategies. Additionally, the movements of infected susceptible hosts are crucial, as they let the disease reach farthest, unaffected areas.
In this study, we combine information on vector suitability and host movement data in a raster-based compartmental epidemiological model to predict disease occurrences and critical management areas. The proposed approach incorporates network analysis features to model the health status of a location (pixel), considering both host movements and proximity-based contagion due to vector-suitable conditions. The model focuses on the “suitable paths”, the shortest paths weighted on the vector’s suitability, linking sources of infection to susceptible hosts within an area.
We used a case study to show the application of this theoretical model and demonstrate the practical utility of this approach in informing mitigation strategies: the Bluetongue Virus serotype 4 (BTV4) epidemic, that occurred in the Sardinia region in 2017.
This research offers valuable insights into the dynamic interplay between network analysis, vector suitability, host density, and host movements, enhancing our ability to predict and manage vector-borne diseases effectively. These findings are essential for public health planning and disease control strategies in a changing global environment
P02.5 The "Land of Fires" Platform for emergency management, data sharing and dissemination
No full textDuring the past years, Campania Region experienced an environmental emergency due to illegal dumping of industrial and domestic waste, often set on fire so the involved area was dubbed “Terra dei Fuochi” (Land of Fires-TdF). This led to deep concerns over population health and over potentially devastating impact on the agricultural market, one of the pillars of the region’s economy (Comba et al., 2006). In such a context, a timely, accurate, and reliable flow of information, aimed both at the population and at stakeholders, is crucial for establishing a proper dialogue between institutions and people, driving the empowerment of citizens.
In response to the emergency, the Italian Government issued the Law n. 6 of 2014, converting the Decree 136 of 2013 (D.L. 2013), and set up an ad hoc multidisciplinary Working Group (WG) to identify the sites affected by spills and illegal disposal on the territory. Moreover, WG defined a process to classify those sites in terms of ban production rather than a limitation to specific crops, consequently, allowing appropriate measures to restore the affected sites.
An adequate infrastructure to support knowledge sharing is a strategic component for efficient and effective emergency management. For this purpose, the Italian Government commissioned the IZS Abruzzo e Molise “G. Caporale” (IZS-Teramo), the development of an IT platform (D.L. 2016) to gather and disseminate standardized data, knowledge, and technologies.
The Land of Fires Platform collects a variety of geographical and non-geographical datasets, in ESRI Shapefile and Excel spreadsheets, which have been processed, and normalized to fit the Oracle 12g database structure. The database was connected to an ArcGIS Server 10.8.1 sharing a ReST and a WMS interface to the underlying data.
The platform front-end is a GeoPortal providing users with various applications and services such as a web application for documents management and a GIS Dashboard allowing viewing results through graphs, tables, and an interactive thematic map. Lands belonging to the municipalities under investigation are also available as OGC WMS and ReST web services in order to maximize opportunities for information reuse. Furthermore, a weekly scheduled procedure publishes updated data on a public repository hosted on the GitHub platform.
In particular, the TdF GIS Dashboard has been realized through the ArcGIS Online Operations Dashboards cloud platform in conjunction with a custom application developed using JavaScript open source libraries.
Despite the technical challenges of fusing data from mixed sources and the potential concerns on data ownership, the platform represents an excellent result of collaboration and cooperation for emergency management and data dissemination and a valid point of reference for the definition of a model of communication between stakeholders and community engagement, that can be reproduced and reused in different areas, at national and international level