Veterinaria Italiana (Journal)
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S09 Landscape phylogeography — using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics
No full textRecent advances in genomics, mathematical modelling and computational biology have enabled evolutionary approaches to become a key component in studying of viral infectious diseases. The use of evolutionary approaches offers many advantages compared to traditional epidemiological methods; for example, they can reconstruct the demographic history of an entire epidemic even when surveillance data are sparse or non-existent, they require only a small number of sampled pathogens, and they can detect linkages among infections in time and space that may not be evident otherwise. Even over short time scales, viruses can accumulate significant diversity, resulting a genomic imprint of the ecological impact on transmission dynamics. By statistically analyzing the genetic differences among viruses sampled from a population, we can reveal the underlying processes that govern viral transmission. Yet, a major challenge in this field is to develop methodologies to formally test the effect of environmental factors on pathogen transmission and evolution. Over the last years, we have addressed this challenge by developing tools in landscape phylogeography, a field at the interface between spatial and molecular epidemiology and that aims to relate phylogenetic informed movements to environmental factors. Specifically, we here present new methods that we developed and applied to investigate the impact of environmental factors on the dispersal velocity, dispersal direction, or dispersal frequency of viral lineages. In particular, we have applied those new analytical approaches to investigate the external factors having impacted several epidemics of public and one health importance like, e.g., the Ebola virus outbreak in West Africa (2014-16; Dellicour et al. 2018), the circulation of Rabies virus in different regions and host species (Dellicour et al., 2019), and the West Nile virus invasion in North America (Dellicour et al., 2020). For instance, our analyses have demonstrated that announcements of border closures were followed by a significant but transient effect on international virus dispersal during the 2014-16 Ebola virus outbreak, and that West Nile virus lineages tended to disperse faster in areas associated with higher temperatures. With our latest developments in the field of landscape phylogeography, we illustrate how to go beyond historical reconstructions and exploit spatially-explicit phylogeographic reconstructions to formally test epidemiological hypotheses
R09.5 Antimicrobial resistance in food animals: priority drugs maps to guide global surveillance
No full textAntimicrobials are life-saving drugs used to treat infections. However, the majority (73%) of antimicrobials sold globally are used in animals raised for food (Van Boeckel et al., 2017) . Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR, as for any disease of global importance, can be guided with maps. However, in low- and middle-income countries (LMICs), attempts at documenting trends in AMR so far relied on summary metrics of resistance across antimicrobial classes. This is a major limitation for potentially taking targeted actions on individual antimicrobial classes.
Here, we mapped AMR prevalence of 7 antimicrobials in E. coli and Salmonella across LMICs, using a geospatial model with data from 1,169 point prevalence surveys. We also developed a novel approach, named co-resistance contextual models, to predict the antimicrobial with the highest probability of its resistance exceeding 50% in the future (2.8 to 6.9 years) at each location on the map. This approach exploited correlations of resistance between antimicrobials to predict the resistance profiles that will most likely occur in the future, producing a map of priority antimicrobials for AMR surveillance.
Hotspots of AMR were predicted in southern and eastern China, Iran, Turkey, northeastern India, southern Brazil, and southern Chile. The highest resistance prevalence was for tetracycline (55%, average across LMICs) and the lowest was for ciprofloxacin (25%). In Africa and South America, 87% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 71% locations were associated with penicillins or sulphonamides, because resistance to tetracyclines has already exceeded 50% in 84% of locations across Asia. Our maps could help to adapt policies to local epidemiological context across LMICs, and provide policy makers with targets –geographic locations and priority drug classes- where scaling up surveillance could carry the greatest benefits
P09.2 GrapeTree integration with spatio-temporal data visualization: a holistic understanding of diseases and the transmission pathways
No full textUnderstanding transmission pathways of infectious diseases plays a crucial role in enabling effective surveillance and outbreak response. Recent advancements in genomic sequencing technology have led to an enormous data volume that can provide valuable insights into the spread of infectious diseases. GrapeTree (Zhou et al., 2018) is a fully interactive tree visualization program for investigating phylogenetic trees plus associated metadata that facilitates the analyses of large numbers of allelic profiles and supports interactive visualizations of large trees, allowing the manipulation of both tree layout and metadata. However, the current capabilities are limited in terms of conducting spatio-temporal analyses due to the lack of the integrated Geographic Information System (GIS) as well as a data visualization system across time. This study aims to address this limitation by developing a web application that integrates GrapeTree with GIS and a temporal visualization system, thereby enhancing its potential.
We leveraged the potential of GrapeTree enabling spatio-temporal analysis of disease transmission. The web application allows users to upload genomic data and metadata, including spatial and temporal data, visualized in the form of a minimum spanning tree. The tree can be manipulated to highlight different aspects, such as transmission pathways or genomic diversity. Effective management of geographic coordinates and temporal data of each sample allows to display them reflecting the selection in the tree on the map and vice versa, and reproducing a timelapse visualization both in the map and in the tree.
The developed web application offers an intuitive and user-friendly interface for the visualization and comprehension of infectious disease transmission pathways. Integrating GrapeTree with GIS and temporal data visualization functionalities, enables the joint analysis of genomic, spatial and temporal data, yielding a more holistic understanding of disease transmission. In particular, timelapse functionalities allow us to visualize and highlight the spread of an outbreak.
The integration of GrapeTree with GIS in this web application constitutes a valuable tool for the surveillance and operational early warning systems of infectious diseases, offering insights into potential outbreak sources and transmission pathways. The application is a fundamental integration in GenPat (https://genpat.izs.it), and in Cohesive Information System (Di Pasquale et al., 2022) (https://cohesive.izs.it) as an interactive dashboard implementing data results from bioinformatic analyses and currently it is used for displaying analyses carried out through Reportree (Mixão et al., 2023). The tool is fully Open Source and available at https://github.com/genpat-it/grapetree-gis. It is also feasible to utilize it directly from the browser without requiring any installation
R06.2 Elucidating African swine fever transmission cycle dynamics at the domestic-wildlife interface. Multihost epidemic modeling in Romania
No full textThe global spread of African Swine Fever (ASF) poses an unprecedented threat to the swine industry. Mathematical modeling has proven useful for quantifying disease transmission and informing control strategies among domestic pigs and wild boars separately. Such models are sufficient for regions where ASF is restricted to a single ecological compartment, however for areas where spillover is suspected—as in Romania where there exists a predominance of low-biosecurity backyard pig holdings—a multihost approach is likely needed. To explain the spatio-temporal infection pattern of ASF in Romania, and to evaluate outcomes from alternative control strategies, a multihost individual-based mechanistic model was developed and parameterized to the period of initial disease spread: June to December 2018. Two types of domestic pig herds were considered in the model: low-biosecurity backyard farms and high-biosecurity industrial operations. Due to the ubiquity of backyard pig farming, each village was considered to be a single backyard farm with locations represented by village centroids. Industrial farms were also represented by their point coordinates. Wild boar presence was simulated via rasterized CORINE Land Cover data sized to estimated wild boar home-ranges, with wild boar presence modeled as a function of raster cell forest density. For initial parameterization, domestic pig herds iterated through susceptible (S), infectious-undetected (Iu), infectious-detected (Id), and recovered (R) states, while wild boar cells were considered to be perpetually infectious following infection, allowing only S-Iu-Id states. Model fitting was performed through Adaptive Population Monte Carlo, a means of approximate Bayesian computation. A total of 24 models were evaluated, with the observed epidemic dynamics being best explained through frequency-dependent transmission between domestic pig units, density-dependent transmission between wild boar cells and domestic pig units, and 2nd order adjacency spread between wild boar cells. Model outputs estimated that a median of 20% of domestic pig unit infections came from wild boar sources, and 30% of wild boar infections came from domestic pig units (https://www.veterinariaitaliana.izs.it/index.php/GEOVET23/article/view/3244/1406). Further, our model estimates that the majority of interhost transmission events occurred during periods of undetected circulation. Alternative control strategy outcomes were evaluated through comparing final epidemic size and relative host contribution, with the biggest decrease in epidemic size occurring through a combination of increasing wild boar surveillance with aggressive local environmental sanitation following initial case detection, and instituting village-wide culling upon ASF case detection in a domestic pig. These results help advance our collective understanding of multi-host pathogen spread to inform animal health policy
P08.3 A novel approach to estimate the animal movement networks: illustration for the swine industry in Iowa (US) and implications for disease prevention and control using a network-based model
No full textAnimal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data sometimes coupled with lack also of detailed information about farm demographics and characteristics presents great challenges for epidemic modeling and prediction. In this study, we propose a new method based on maximum entropy to generate “synthetic” animal movement networks that resemble the “real” networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrate our method for the pig movement networks in Iowa, where we had more “real” data to validate our approach. We then performed network analyses to gain insights into the generated pig movement networks and applied the generated networks to a network-based epidemic model to identify potential vulnerabilities of the network in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is currently quite concerned about this disease. ASF outbreaks starting from random farms were associated with highly variable outcomes, ranging from no spreading to large outbreaks. However, outbreaks originating from high out-degree farms may lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and more cost-effectively prevent and control disease transmission. Our approach could be easily adapted to any other livestock system or disease. Future work will include sensitivity analysis to evaluate the impact of our assumptions in the “synthetic” swine movement network and validation of our approach with real-world swine movement information obtained from several production systems in the US
P06.2 Improving the efficacy of surveillance in wild boars of South Korea
No full textSince African swine fever (ASF) was confirmed in South Korea in September 2019, the disease keeps spreading primarily in wild boars. The key element contributing to this situation is the lack of identification of risk factors lead by surveillance bias. The high-elevation mountainous terrain covering 70% of the country makes search challenging. Inadequate application of surveillance programs to this unique orography results in missed cases and also poses a threat to the swine industry. Identifying potential under-surveillance areas would contribute to early detection of disease and improve the current surveillance system.
The directional distribution analysis was first performed to characterize the spatiotemporal distribution trends of ASF-positive wild boars. Then combinations of geospatial analysis and statistical approaches were applied to estimating potential under-surveillance areas. For South Korea, another important surveillance subject is the river shared with North Korea in terms of the continuous source of the virus introduction. While these countries are separated by the demilitarized zone serving as a physical barrier to the wild boar's movement, several rivers spanning both countries allow for the influx of infectious agents. The statistical analysis was performed to determine the relationship between ASF-positive wild boars and water, and then hydrologic analysis was applied to identify points for monitoring the risk of ASF spread.
The directional distribution analysis indicated a gradual shift in the center of ASF occurrence to the southeast, most likely due to the wild boar’s movement. At the same time, several groups possibly infected by other routes were observed in distant areas.
Pearson's rho test indicated that elevation (rho= -0.908, p-value < 0.001) and distance from roads (rho= -0.979, p-value < 0.001) may have a significant impact on limiting surveillance activities, while the quality of available habitat for wild boar (QAH) map was shown to be useful in South Korea to indicate the probability of wild boar presence (rho = 0.786, p-value = 0.036). The map of potential under-surveillance areas was created considering these results, and was validated by a chi-square goodness-of-fit test (X-squared = 208.03, df = 1, p-value < 0.001).
The strong negative correlation between ASF-positive wild boars and distance from water sources (rho= -0.997, p-value <0.001) suggests that areas around rivers are one of the priority areas for monitoring. Furthermore, the hydrological analysis provided strategic surveillance points to monitor the risk of ASF spreading through the water inside South Korea as well as the continuous risk of ASF entry from North Korea.In this study, we have developed tools to improve surveillance activities for the early detection and warming system of ASF. The identification of potential under-surveillance areas as well as missed risk factors can greatly improve monitoring systems. Thus, more effective and efficient countermeasures can be implemented
P10.09 Cluster analysis of Bovine TB using multiple methods, and considerations when constructing a Composite Index
No full textMultiple methods were employed to analyze spatial clustering of Bovine Tuberculosis (TB) breakdowns in cattle across the High Risk Area (HRA) of England using data from 2013-2021. Located in South-West England and the West Midlands region, the HRA is one of three TB Risk Areas in England, and is often associated with the majority of TB incidents in the country (APHA, 2022). These methods employed Spatial Statistics tools and Space Time Pattern Mining tools in ArcGIS Pro, use of SaTScantm, and ETL processing using FME and Model Builder.
The first method built on techniques applied to data from the Edge Area of England by Downs et al. (2021), utilizing circular 25km2 spatial units and set cut-off criteria for three rolling 7-year periods to determine the strength of evidence for a local reservoir. The circular spatial units aligned to a grid of centroids 2km apart, creating overlap between the spatial units, and addressing potential uncertainty as to the boundary of any reservoir found with contiguous spatial units.
The second method utilized the same circular 25km2 spatial units but this time estimated TB infection using herd incidence per 100 herd-years at risk. Initially this was calculated per circle, and later applied the nearest 100 herds to each circle.
The third method used Hot Spot Analysis via the Getis-Ord Gi* statistic to identify statistically significant hot and cold spots of TB per year across contiguous hexagonal 25km2 spatial units. These same spatial and temporal units were used in Cluster and Outlier Analysis using the Anselin Local Moran’s I statistic as the fourth method. TB breakdown hot spots and cold spots were identified, as well as statistically significant spatial outliers. For both the Getis-Ord Gi* and Anselin Local Moran’s I statistics, parameters were tested using ArcGIS’s Conceptualization of Spatial Relationships to optimize cluster identification.
The fifth method involved Space Time Pattern Mining and implemented the Getis-Ord Gi* and Anselin Local Moran’s I statistics across a space-time cube of comparable spatial and temporal extent.
The sixth method utilized SaTScantm to analyze the space-time clustering of TB breakdowns for five biennial periods within the time of the study. Clusters were identified and then ranked by their statistical significance for each period.
Spatial and spatio-temporal correlation and difference were noted across the different methods, and Composite Indexing was investigated for its viability in combining the multiple methods into a single index. The methodology for integration of spatial and temporal units and extents is described, noting the importance of expert opinion and degree of subjectivity in parameter selection when creating an index
P02.8 Early warning of bovine tuberculosis for veterinarians using spatio-temporal models
No full textBovine tuberculosis (bTB), caused by infection with Mycobacterium bovis is endemic in cattle in many countries worldwide including Ireland. The incidence rate in Ireland varies by herd and location and it is hoped that statistical disease-mapping models accounting for both spatio-temporal correlation and covariates might contribute towards explaining this variation. The final goal of this work is to produce a user-friendly, near real-time application that incorporates predictions from these models along with other metrics to serve as an early warning system to aid veterinarians in the field. Implementing different spatio-temporal random-effects models (e.g., negative binomial Besag-York-Mollié), we explored the association between covariates and the number of bTB cattle at an areal level by dividing Ireland into equally sized hexagons and determining the best fitting model. Data from the national bTB eradication programme was utilised. Models were fitted in a Bayesian framework and estimates were obtained using the integrated nested Laplace approximation (INLA) approach. We found that spatial models that accounted for spatial dependency offered a statistically significantly better fit in comparison to non-spatial versions where independence between hexagons was assumed. As an epidemiological tool, we have developed and improved on previous iterations, an interactive online dashboard to explore results. By developing a user-friendly, interactive dashboard, it allows the results from complex models to be presented in an accessible manner for veterinarians. The outstanding challenge is to fully automate the dashboard so that near real-time data is being used so that it is most beneficial for the bTB eradication programme
K01 The challenges of real-time science communication and decision support in a pandemic context: building the plane instruments after take-off
No full textThe Covid-19 pandemic took most countries by surprise with relatively little preparedness in terms of equipment, disease diagnostic tools, or decision-support systems. Based on the experience of being at the forefront of the Covid-19 pandemic management in Belgium, the talk will describe and discuss the most important challenges in terms of public communication and decision support. More specifically, the talk will address the multiple consequences of a low general proficiency in health sciences in the public and decision makers, the difficulty in handling rapidly changing data and knowledge while maintaining good levels of trust, the critical need to carefully organize the much-needed scientific debate, and the overall conditions of an effective collaboration between scientists, decision makers and the media in the context of a crisis
P03.1 Evaluation of Sea Surface Temperature trend across aquaculture sites in Central Adriatic Sea
No full textWater temperature is an important variable in aquaculture as growth and survival of aquatic animals and molluscs strongly depend on it. Water temperature can be measured through satellites by using the infrared part of the electromagnetic spectrum and capturing the thermal emission. Satellites dedicated to Earth Observation (SEO) regularly acquire information both on land and at sea.
The aim of this study was to collect analysis ready data derived from Earth Observation to investigate the trend of Sea Surface Temperature (SST), over the last 15 years of the coastal waters facing Abruzzo Region. Knowing the trend of sea temperature could help shellfish farming management, as temperature is a key factor for the growth and survival of all organism. To provide aquaculture with useful information, the SST values were extracted at the central point of the farms, for both existing and potential sites.
Among the available satellite-derived products, the “Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis" was chosen from the CMEMS repository of European Copernicus programme as a good compromise among spatial accuracy, temporal frequency and temporal coverage (Buongiorno Nardelli et al., 2013), besides undergoing gap-filling procedure.
The data spanning from 2008 to 2022 was acquired and processed using customized procedures developed within the framework of open-source software, specifically R and QGIS.
A time series analysis method was applied to the average daily values of the SST identifying the components of the additive model: trend, seasonality and random effects. The significance of the trend was evaluated through the Mann-Kendall statistical test for all farms, most of which showed a statistically significant increasing trend. A linear regression analysis applied on trend data (excluding seasonality and background noise), estimated an annual average temperature increase of 0.010 degrees Celsius over the given period (Filipponi et al., 2017).
The SST seasonality follows the atmospheric temperature one, with colder winters and hot summers in the study area.
Climate change represents a potential limiting factor for the breeding of bivalve molluscs by directly interfering with their survival and their behaviour. It has been demonstrated that the increase in temperature is also one of the main factors triggering events that lead to the accumulation of some biotoxins in European bivalves (Dhanji-Rapkova et al., 2023).
Continue monitoring the surface temperature of the seas is of fundamental importance for assessing the ecosystem health and evaluate the resilience of marine ecosystems. Satellite Earth Observation could greatly help in this objective, providing free data of sea surface waters, remotely detected, with high frequency and high accuracy