Veterinaria Italiana (Journal)
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R09.2 Geographical isolation in an interconnected world: application of phylodynamic and phylogeographic methods for the investigation of Porcine circovirus type 4 history
No full textOver the years four porcine circoviruses (PCVs) species have been discovered, which apparently share common epidemiological trajectories. In particular, PCV-2 and PCV-3 species, better known for their pathogenicity and economic impact, despite being reported as emerging new viruses, were thereafter proved to have been circulating undetected over several decades in domestic pigs.
The detection of PCV-4 in China in 2019 (Zhang et al., 2019) and retrospectively in samples from 2012, demonstrates a circulation for at least 10 years in Chinese domestic pigs, reminding PCV-2 and -3 scenarios. However, the current restricted distribution in China, South Korea, Malaysia and Thailand, and the lack of detection in Italy, Spain and Colombia (Franzo et al., 2020, Vargas-Bermudez et al., 2022), apparently suggest a recent origin, thus conflicting with what demonstrated for the ubiquitous PCVs-2 and -3.
The present study aims to investigate PCV-4 apparent geographical confinement and address questions about its actual epidemiology and emergence.
Phylogenetic, phylodynamic, and phylogeographic analyses were performed on PCV-4 sequences with adequate metadata available in GenBank to reconstruct its history, migration paths and evolution.
A total of 97 sequences were included in the data set: 88 were detected in 10 different Chinese provinces, 4 in South Korea, 3 in Thailand, and 2 in Malaysia. The achieved alignment and phylogenetic trees showed a certain genetic stability between strains over time, and congruent results were obtained for different genomic regions. Additionally, the evolutionary rate is of the same order of magnitude of PCV-3. The analysis of viral population dynamics, similarly to other PCVs, suggests an ancient origin followed by persistent but undetected circulation in the swine population. Based on these results and others PCVs history/epidemiology a wider distribution than the one currently shown in literature was expected. On the contrary the geographical clustering, and the low number of clearly supported migration rates among Chinese provinces respectively showed by phylogenetic and phylogeographic analysis are compatible with the geographical isolation suggested by the detection of PCV-4 in Asia only and its absence in pivotal swine farming countries such as Italy and Spain. The contradiction with the hypothesis of an ancient viral origin is challenging to be explained, although the currently scarce number of available sequences, the limited number of studies performed outside Asia and the short timespan considered may be also involved.
This study provides an example of the application of genetic methods as instrumental tools to improve the understanding of pathogens' epidemiology, geographical relations, and emergence. In presence of larger-scale geographical data, genetic and spatial methods may act in synergy to better investigate the presence of spatial patterns, and to monitor, predict and thus better control the spreading of animal infectious diseases
R01.4 How to combine spatio-temporal information and Danger theory for animal disease surveillance?
No full textEvent Based Surveillance (EBS) systems such as HealthMap, Promed and PADI-web are used daily to detect outbreak events reported in web articles. Once the articles are collected, these systems rely on human moderation, and implement supervised classification algorithms to classify articles according to their relevance (Valentin et al., 2020). Applying such supervised methods can be challenging, as epidemiological datasets have an imbalanced class distribution, and because the annotation task, which is critical to the success of these models, can be expensive and time consuming. Another important limitation of EBS systems is that the drivers of disease transmission (e.g. disease characteristics, environmental and epidemiological risk factors) are not always found in textual data and are therefore not taken into account by EBS systems (Kim & Ahn, 2021).
In this context, we propose an unsupervised approach that relies on the spatio-temporal information of the reported epidemiological events, to classify articles while taking into account the environmental factors associated with disease onset through risk mapping. This method, called EpiDCA, is an adaptation of the Dendritic Cells Algorithm (DCA), inspired by the danger theory (Greensmith et al., 2008). EpiDCA is characterized by expert-defined parameters, making it applicable to different diseases and environmental contexts. The proposed method was first tested and evaluated using PADI-web and HealthMap datasets related to avian influenza (AI) in Asia between 2018 and 2019, and a suitability map for AI produced for the same area. To measure the accuracy of the model, we calculated the precision, recall and F-score. EpiDCA achieved a very good performance with an F-score of 0.70 and 0.90 for an imbalanced and a balanced dataset respectively. The results confirmed that considering disease risk factors is a good approach in event classification. EpiDCA was then compared with state-of-the-art supervised machine learning methods and appeared to be competitive.
To test the robustness of the method we then applied EpiDCA for the same disease (AI) in a different geographical context. We used PADI-web dataset related to AI in France between 2022 and 2023 and the corresponding risk map. In this case, events were reported at different levels of spatial granularity (country, region, department and city, from the lowest to the highest granularity). The consistent results confirmed that this method is robust, and the best performance in terms of precision, recall and F-score was observed when events were reported at the department level.
In the future, additional tests will be conducted on different datasets and case studies (a cross-border disease such as African Swine Fever, and a vector-borne disease such as West Nile fever disease) to evaluate the genericity of the proposed method.
R02.3 Geospatial Methods for Reporting During the United States 2022-2023 Highly Pathogenic Avian Influenza (HPAI) Outbreak and Ongoing Emergency Response Preparedness
No full textThe 2022-2023 U.S. HPAI outbreak has affected an unprecedented 836 commercial poultry premises across 47 states. Mapping is being used extensively to aid in control and containment, as well as situational awareness and reporting to international trading partners. Due to the extent of the outbreak, the United States Department of Agriculture (USDA) has developed and employed numerous automated mapping methods for the first time in a U.S. animal health emergency to meet reporting and response needs. Daily Situation Report maps are delivered to all affected states using a secure GIS portal. An online operational dashboard reports current metrics (number and types of cases, affected counties, epidemiological curve, etc.) and maps to help inform policy and decision making. Confirmed HPAI detections in wild birds are displayed in a publicly available online mapping and reporting tool. Automation using Python scripting is critical to efficiently process data collected by field personnel and is used in each of these applications. These developments have enabled a small team of mapping professionals at the USDA Center for Epidemiology and Animal Health (CEAH) to provide critical map products, geospatial data, and more to a wide variety of audiences including internal responders, leadership, trading partners, researchers, and the public.
P04.9 Avian Influenza at the Wild Waterfowl – Domestic Poultry Interface
No full textHighly pathogenic avian influenza (HPAI) is a growing risk for both wild birds and the poultry industry. The 2022–2023 HPAI H5N1 outbreak in the United States is the largest and most costly animal health event in U.S. history. Based on the results of full genome sequencing, wild bird introductions have been the primary mechanism of introduction of virus into poultry operations. As prevalence increases in North America, better tools are needed to assess changing risks associated with HPAI spillover from wild animal populations into poultry operations. Through an interagency partnership, we have created a series of spatially explicit models for the United States to assess the prevalence of avian influenza in various waterfowl host species and the relative risk of spillover events at poultry operations across different times of year. These models account for not only the number of waterfowl in an area, but also species-specific prevalence rates to determine the effective waterfowl population or presented disease risk. When paired with information on the distribution of poultry farms, we can identify an area’s risk of spillover. Preliminary models have been validated using genetically identified wild bird introduction events from the current outbreak and have performed well at predicting spillover events during the 2022 HPAI outbreak. Over 90 percent of wild bird introductions occurred in areas with predicted moderately high and high spillover risks. Next steps will include incorporating climate, landcover, and anthropogenic inputs and using phylogenetic analyses of the current H5N1 outbreak.
Preliminary results are publicly available in an interactive model data visualization tool and allow for investigation across spatial scales at specific locations, for more informed decisions regarding overall risk, monitoring, and prevention.  
P08.4 Spatial Analysis of A Viral Agent First Time Appeared In Turkey By Geographic Information System: Cattle Noduler Exhantem (Lumpy Skin Disease-LSD)
No full textSpatial analysis are of increasing importance in health researches in terms of the distribution of health incidents, collecting data about the regions where they are clustered. The aim of our study is to investigate spatially notifiable Lumpy Skin Disease (LSD), causes noduls on the skin, outbreaks first seen in August 2013 in Turkey and turned into endemic outbreak between 2013 and 2017, to determine the regions where they are clustered, to assess the patterns during the years. Geographic information system (GIS) software QGIS™ was used for data analysis and SaTScan™ was used for cluster analysis. Clusters were determined by the space-time permutation model. 1552 LSD outbreaks data obtained from OIE and Turkish Ministry of Agriculture and Forestry were used. During the 5-year period, outbreaks were observed in 68 provinces, 340, districts and 1289 villages. The most outbreaks were in 2014 (n=784), the least outbreaks were in 2017 (n=17), the most cases were in 2016 (n=3258) and the least cases were in 2017 (n=130). The most outbreaks were reported in Sivas province (n=164) and the most outbreaks were reported in August (n=249). The clustering analysis showed that cases were clustered in different regions by years. Statistically significant clusters were detected in provinces where the population of dairy cattle is high, with a humid, warm climate and close to countries such as Syria, Iran, Iraq and Georgia. Outbreaks is observed to be concentrated around Asi, Kizilirmak, Murat and Ceyhan rivers on the created outbreak maps. Total of 4524 animals were culled for five years and the highest culling occurred in 2016 (n = 2717). After the least outbreak occurred in 2017, LSD outbreaks started to rise again in 2018 (n = 53). Culling is seen succesful way against to LSD. It is recommended that the protection provided by the sheep-goat pox vaccine against LSD needs to be revised. This is the first published study conducted cluster analysis with LSD outbreaks seen in Turkey
P02.9 First attempt to develop risk indicators for earthquake impact assessment on animal health and welfare for the preparation of contingency plans and implementation of the Information System for the Management of non epidemic veterinary emergencies
No full textNon-epidemic emergencies caused by major adverse events, like earthquakes, can have immediate and long-term impacts on human as well as on animal health and welfare. Estimating possible impacts induced by seismic events is a crucial point for planning and preparing mitigating actions. Literature is scarce regarding the existence of indicators aimed at estimating the allocation of resources for preserving animal health and welfare in a seismic risk scenario. The aims of this pilot study were: to estimate the impact of an earthquake on animal health and welfare through the identification of all hazards related to, as prerequisite for targeting interventions and quantifying human, instrumental and financial resources and to build relevant indicators for risk mitigation as relevant tools for Veterinary Authorities involved in the management of non-epidemic veterinary emergencies, especially in the seismic one. 31 experts from research, Veterinary Public Health fields and Civil Protection were enrolled in a first Expert Knowledge Elicitation that was carried out according to the rules of the Appreciative Inquiry Summit, a model built on 4 phases - discovery, dream, design and destiny that was applied in plenary elicitation sessions alternating with 4 subgroups elicitation sessions guided by 7 facilitators. The 4 subgroups were composed on average of 8 experts, suitably balanced with respect to professional profiles and respective affiliations were guided in the discussions by one or two facilitators. The results achieved by each of the 4 subgroups were presented in plenary, they were extensively compared and debated. A list of hazards with related causes, interventions and resources needed was drafted. At the end of the Expert Knowledge Elicitation phase, each of the 4 subgroups was tasked to build relevant indicators according to the hazards identified. Overall, 23 risk indicators were identified. Of these, 14 were classified as belonging to mitigation, prevention and preparedness phases, that is in peacetime or defined as pre-earthquake indicators, 9 indicators were attributed to the response and recovery phases or defined as post-earthquake indicators. To the best of our knowledge this is the first study aimed at identifying useful indicators for the management of earthquakes in the veterinary field to be included in the national contingency plan in accordance with the WOAH “guidelines on disaster management and risk reduction in relation to animal health and welfare and veterinary public health”. The risk indicators represent a tool needed for the implementation of the National Information System for non-epidemic veterinary emergencies (SIVENE) web GIS, namely for emergency planning, response and recovery to facilitate decision-making process at local, regional and national level; they could have potential applicability at national level being derived from a representative group of experts and the majority of them could be also extended to other type of disasters
P04.1 Investigation on the presence of Hepatitis E virus in different animal species populations in Abruzzo and Molise Regions during the 2015-2021 period, Italy
No full textHepatitis E virus (HEV) is a zoonotic pathogen, causing infectious hepatitis in man. Pigs and wild boars are the natural asymptomatic reservoirs, while the disease in humans could be either asymptomatic or evolve in hepatitis. In Europe, an increasing number of human infections from HEV has been reported over the last years. Up to now, HEV prevalence in Italian northern Regions has been extensively determined in wild boars and pigs, while less data have been collected from the central-southern ones, especially in wild animals. Moreover, the Abruzzo Region located in central Italy is considered a hot spot for HEV human infections and Molise Region, located in southern Italy and close to Abruzzo, should be also taken in consideration. The aim of this study was to investigate the circulation of HEV in different animal species from Abruzzo and Molise Regions, central-southern Italy. Liver, gallbladder and faeces samples were collected from pigs coming from closed farms or kept free-ranging and from hunted wild boars. Liver samples from other wild species were also considered. Overall, RNA from HEV virus was detected in 336 samples out of the 4,486 collected during the 2015-2021 period (7.5%, CI 6.8% - 8.3%). Animal species in which HEV genome was detected were wild boars (10.3%, CI 9.2% - 11.6%), domestic swine (5.6%, CI 4.6% - 6.9%), roe deer (2.2%, CI 1.0% - 4.8%), deer (2.4%, CI 0.7% - 8.3%), and badger (1.7%, CI 0.4 – 9.1). The positivity in liver samples was higher than in faeces, which was higher than in gallbladder. The higher number of positive samples was found in wild boars, and especially in liver. The results of this study suggest that HEV infection seems to be endemic in the wild boars living the two Regions under study. The detection of HEV RNA in other non-target species, such as roe deer, deer, and badgers also warrant consideration in surveillance programs and in estimating the risk for HEV human infections
P08.5 Network Analysis and Modelling for Prevention and Control of Equine Infectious Diseases in Italy: A Data-Driven Approach
No full textThe equine sector in Italy is of great importance and contributes significantly to the country's economy. However, it faces numerous challenges in preventing and controlling infectious diseases, which can have devastating effects on horse health, welfare, and the equine industry as a whole. The Italian Ministry of Health-funded research project (2021-2022) aimed to assess the vulnerability of the equine sector to disease incursions such as the African Horse Sickness Virus (AHSV) or the spread of Equine Infectious Anaemia Virus (EIAV), and others ( Dominguez et al., 2016; Fairbanks et al., 2022).
Through network analysis, spatial-temporal analysis, and machine learning techniques, we identified high-risk areas, periods, and strategic nodes within the Italian equine network to be targeted for preventing and controlling the risk of incursions from abroad and the internal spread of diseases.
Machine learning approaches combined national entomological surveillance data with satellite-derived temperature time series to determine if establishments introducing animals from abroad were suitable for AHSV vectors. The Random Forest algorithm achieved an 84% accuracy in this task.
Spatio-temporal clustering identified significant clusters of importing establishments, primarily in northern Italy, central Italy, and the Lazio Region, with equestrian sport, recreational equestrian, and riding schools being the most at-risk sectors (comprising 76% of high-risk activities). Spring and summer were identified as the most at-risk seasons (Martínez-López et al., 2011). To assess the size and speed of a possible epidemic's spread on the internal movement network, we calculated the Giant Strongly Connected Component (GSCC). This encompassed 33% of the network's nodes, evenly distributed throughout the country. Within the GSCC, approximately 2.5% of the nodes were identified as super hubs, i.e. nodes exhibiting high values of both degree and betweenness. These establishments play a crucial role, as they can infect many nodes directly connected or act as bridges between groups of nodes, thus facilitating rapid and vast hypothetical epidemics. Moreover, 1% of all establishments proved to be spatial super-spreaders and or super-susceptibles, that is to say, they can spread the infection over long distances.
The analysis conducted to prevent the spread and introduction of equine diseases in Italy has been complemented by EquiTracing, a disease control web tool. This integration significantly enhances efforts to contain the spread of equine diseases. By utilizing cutting-edge technology, it enables real-time access to data from the National Animal Disease Reporting System (SIMAN) and the Equine Movement Database (BDN-E).
EquiTracing empowers authorities to trace equine movements, identifying infection sources and contact chains. It offers interactive tables, maps, graphs, querying and analyzing tools, tracing animals or batches, and generating dynamic reports.
In conclusion, these results start an ongoing research journey. The data-driven approach and knowledge aid informed decisions and efficient prevention plans for equine diseases
K03 Decision-support tools to track and predict climate-sensitive infectious diseases at cascading spatial scales
No full textExtreme climatic events, environmental degradation, unplanned urbanization, and socio-economic inequalities exacerbate the risk of infectious disease emergence, spread and transmission. For example, mosquito-borne diseases, such as dengue and malaria, are highly sensitive to climate variability and climate change. A warming climate can lengthen the transmission season and alter the geographical range, potentially bringing diseases to regions which lack either population immunity or strong public health infrastructure. More frequent extreme weather events, such as storms, floods, and droughts, also affect the timing and intensity of vector and water-borne disease outbreaks. Despite the health threats of rapid environmental change, we lack the evidence-base to understand and predict the impacts of extreme events and landscape changes on disease risk, leaving communities vulnerable to increasing health threats. This talk will focus on the past, present and future risks of emerging infectious diseases and describe the partnerships, data, and tools required to build climate resilience in climate change hotspots, including cities, the rainforest, highland areas, and small islands, to improve preparedness and response to emerging infectious disease threats and assist public health services adapt to climate change
K07 Issues and challenges in fair and robust AI models
No full textArtificial Intelligence (AI) and Deep Learning have emerged as revolutionary technologies that have transformed various industries and are driving innovation across the globe. However, as AI and Deep Learning continue to evolve and find applications in numerous domains, they face several significant challenges and issues. One of the foremost challenges is the reliance on data, especially the quality and quantity of data. AI and Deep Learning models are heavily dependent on large datasets for training and fine-tuning. Weak annotations and biases in the data can lead to profound consequences, affecting the performance and fairness of these systems. Moreover, the availability of different sources of data to be integrated in a common AI model is crucial to let the AI model to mimic real complex systems. The talk will explore all these challenges, delve into the pressing issues related to weak annotations and biases in data, shedding light on their implications for the development and deployment of AI technologies in society, and propose some strategies to address them.