Veterinaria Italiana (Journal)
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P09.1 What can we learn from phylogeographic analyses of SARS-CoV-2?
No full textIn the field of molecular epidemiology, genetic analyses of pathogens are used to complement traditional epidemiological methods in various ways. For instance, genetic analyses offer the possibility to infer linkages between infections that are not evident without analysing viral genomes. In particular, the development of phylogeographic methods has enabled to reconstruct dispersal history of epidemics in a discretised or on a continuous space, using only a relatively limited number of viral sequences sampled from known locations and times. Since the beginning of the COVID-19 pandemic, phylogeographic analyses have been conducted to reconstruct the dispersal history of SARS-CoV-2 lineages at global and more local spatial scales. I here illustrate the interest of phylogeographic inference by presenting analytical approaches that we implemented and applied to address some specific questions related to the epidemiology of SARS-CoV-2: the relative importance of introduction events to establish local transmission chains (e.g. at a country level), the impact of international travels during European summer holidays 2020 on the resurgence of COVID-19 in Europe, the invasion dynamic of the Alpha variant that emerged in England, and the variant-specific dispersal dynamics of SARS-CoV-2 in New York City. In these recent studies, we specifically aimed to go beyond historical reconstructions and further exploit phylogeographic reconstructions to formally test epidemiological hypotheses.
 
P08.1 Beyond contact rate for assessing pathogen transmission: implementing a movement-driven model in exposure risk inference
No full textInteractions between individuals from different species are highly relevant in the transmission of pathogens in multi-host systems. In this regard, we can distinguish between direct interactions, which involve contact, and indirect interactions, mediated by the environment or vectors. Various technologies have been employed to study animal interactions, such as proximity loggers, GPS tracking devices and/or camera traps. However, the subsequent analysis of these data to estimate risk transmission is often reduced to interaction rates and the probability of transmission given interaction. Thereby, a continuous spatio-temporal process is simplified and discretized, overlooking the influence of interaction characteristics and the pathogen's relationship with the host and the environment.
In this study, we applied a novel movement-based model that considers the interaction duration -by means of the number of GPS locations where the interaction occurs and the time interval between them-, the temporal window between consecutive locations of different individuals, a continuous pathogen decay in the environment until its survival period in the environment is fulfilled, and the excretion and acquisition rates of each host species. Therefore, each interaction has unique characteristics, and consequently, a unique transmission risk. Using animal tuberculosis (Mycobacterium tuberculosis complex) as disease example, we analyzed GPS data from 54 collared wild (15 red deer [Cervus elaphus], 7 fallow deer [Dama dama] and 12 wild boar [Sus scrofa]) and domestic animals (8 pig and 12 cattle) in two management systems in Spain where this disease is prevalent: a national park and an area with extensive free-ranging pig and cattle farms. We considered an interaction to occur within the GPS error margin (12 or 26 meters, depending on the GPS device) and a pathogen survival period in the environment of 4 to 12 days, depending on the area and season. We compared the results with those obtained for the same data in previous studies based just on the quantification of interaction rates. As a result, wildlife species identified as most involved in pathogen transmission to livestock differed between the two approaches, particularly in the national park. Among the three considered wild species, fallow deer had the highest interaction rate with livestock according to the traditional approach, but with the new approach, wild boar took the lead as posing the highest transmission risk to livestock. These results indicate that failing in considering the specific conditions of each interaction can imply a misidentification of key species in pathogen transmission. In addition, we found that wildlife poses a greater risk to livestock than livestock to wildlife in both studied systems, emphasizing that disease control measures should be implemented in wild populations in addition to those already established for livestock
P09.4 The Phylodynamics of SARS-CoV-2 Delta and Omicron Variants between 2021 and 2022 in Kuwait: The Phylodynamics of SARS-CoV-2 in Kuwait
No full textThe rapid emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in unparalleled public health and economic implications worldwide. In Kuwait, the virus was first detected in February 2020, after which it rapidly spread and caused over 600 thousand infections, reinfections, and over 2500 deaths until early 2023. While the pandemic has passed its emergency stage and been declared endemic, continuous molecular surveillance for rapidly evolving pathogens like SARS-CoV-2 is critical for early intervention efforts, as the emergence of new variants is the most essential component in shaping the dynamics of the pandemic. Thus, the main objective of this study is to investigate and compare the variant-specific evolutionary epidemiology of all the Delta (B.1.617.2 and descendant lineages) and Omicron (B.1.1.529 and descendant lineages) complete genome sequences collected between 2021 and 2023 in Kuwait. Here, we reconstructed, traced, and compared the two variants' demographic, continental phylogeographic origins, and the role of sex and age in shaping their transmission history between infected patients using a discrete-trait Bayesian phylodynamic analytic pipeline. We found that the Delta variant had a higher evolutionary rate than the Omicron variant. While both variants went through periods of sequential growth and decline, likely linked to air travel, intervention measures and the development of natural immunity. Our results indicate that the delta variant and descendant lineages were frequently introduced into Kuwait from Asian countries between mid-2020 and early 2021, while those of the Omicron variant were most likely from Africa and North America between late 2021 and 2022. For Both variants, our between-patient discrete-trait analysis revealed statistically significant (Bayes Factors > 1000) asymmetric unidirectional and intense transmission routes from adult patients aged between 20 and 50 years on one side and patients aged less than 20 and older than 50 years on the other. In contrast, no significant evidence was found for the role of sex in shaping the evolutionary transmission history of both variants between males and females. Our analysis highlighted the importance of implementing risk-based genomic surveillance programs to uncover important epidemiological aspects of SARS-CoV-2 that may improve the efficiency of future decision-making related to allocating intervention resources toward relevant emerging variants. Finally, we discuss the importance of unifying molecular surveillance efforts in humans and companion animals to improve the inferences derived from our phylodynamic models
P10.4 Clusters and risk factors of bovine tuberculosis in a Mexican state
No full textBovine tuberculosis (bTB) is a chronic infectious disease widely distributed in the cattle population in Mexico, a campaign against bTB was officially stablished in 1996, but up to now few and isolated epidemiological studies of the disease have been carried out in the country. Information on the status of bovine tuberculosis in SLP available from academic studies and public data is limited, and no attempts have been made to characterize the spatial distribution of bovine tuberculosis in the state to date. A better understanding of the disease spatial distribution and the factors associated with increased risk is required to implement more effective control measures.
A cross-sectional study based on bTB testing on >1,600 herds in two regions of San Luis Potosi state, Altiplano and Huasteca, during 2018-2021 was conducted for the identification of spatial clustering and of bTB risk factors using the scan statistic test and multivariable logistic regression models.
The proportion of herds with at least one reactor was 17.9% in Altiplano and 38.6% in Huasteca, with one high risk cluster present in each region, that comprised 27.7% and 57.4% of the positive herds. Larger herds (>20 animals) and those included in the spatial cluster were exposed to a significantly increased risk of having at least one reactor in both regions.
Results demonstrated the presence of certain herds exposed to higher risk depending on their size and location in both regions, suggesting similar patterns of transmission; these findings can be used to further investigate how the disease spreads in San Luis Potos
R04.1 Using joint species distribution models to improve predictions of co-infection in veterinary epidemiology
No full textPredicting pathogen distributions is vital for risk assessment and disease control in wild and domestic animal populations. Co-infections are of particular clinical and epidemiological significance, as they can often be associated with increased disease severity and pathogen shedding, increasing both the burden of disease and the risk of spillover (Peel et al., 2019). Moreover, pathogens can also interact and affect the likelihood of subsequent infections (Fountain‐Jones et al., 2019). Yet, when predicting pathogen distributions, research overwhelmingly focuses on single infections caused by individual pathogens. This univariate focus means that important biotic effects are often excluded in distribution models, affecting both the reliability of inferences and the accuracy of predictions. Multivariate models help overcome this hurdle by accounting for biotic associations between pathogens. To explore avenues for improving distribution modelling in veterinary epidemiology, our primary objective was to understand the wider use of multivariate models in co-infection research. From these findings, we make recommendations on how Joint Species Distribution Models could be used by veterinary epidemiologists.
To understand the wider use of multivariate models in co-infection research, we conducted a systematic review of the literature using a suite of terms relating to both multi-response models and co-infections. This yielded 746 unique peer-reviewed research articles. Using seven pre-defined exclusion criteria, we identified 75 peer-reviewed primary studies that jointly measured infection patterns with two or more endo-pathogens of humans or animals in natural environments. Only 27% studied co-infections in animal hosts. Using a generalised linear model to explore how model choice may vary with study goals and purpose (i.e. inference or prediction), we found a strong association between model choices and study goals. Studies that sought to predict the spatial distributions of co-infections typically employed multinomial models, ignoring pathogen associations. Clustering analysis based on study features and a citation analysis to investigate rates of knowledge exchange both identified distinct clusters for multinomial versus multivariate model users. We conclude that biotic associations are often ignored when predicting co-infections and that there exists a lack of knowledge exchange among co-infection researchers with different research agendas. Moreover, we show the untapped potential for using Joint Species Distribution Models in veterinary epidemiology and how one can go about selecting the most appropriate model for complex data for improving predictions of co-infection distributions (Powell-Romero et al., 2023), as well as highlight the importance of interdisciplinary collaboration
P04.2 Best options to apply MCDA in an environmental context
No full textMulti-criteria decision analysis (MCDA) is a sub-discipline that blends spatial geographic data and its weighting, to transform them into a decision map. It is for the most part used in land suitability, epidemiological risk area assessment, and environmental studies.
The scope of the study was to describe how MCDA has been used in an environmental context in the last five years. The research questions of the study are: (i) what are the most used MCDA methods in the environmental context? (ii) what kind of criteria are used to create maps? (iii) what are the most used MCDA methods based on the available criteria?
We conducted a rapid literature review, which followed the Cochrane guidance. The methodology included five steps: (i) elaboration of the research question; (ii) identification of peer-reviewed articles published from January 1st, 2018, to March 6th, 2023; (iii) definition of criteria for the inclusion and analysis; (iv) charting of the information obtained; (v) summarizing and reporting the results.
The rapid review search identified 1364 articles. After duplicate elimination, 1362 abstracts were screened for relevance and pertinence. 91 full-text articles were assessed for eligibility. Finally, 21 articles were included in the review. The articles concern communicable diseases (13), natural disasters (4), vectors (2), and environment (2). The most used MCDA methods in the articles are the analytic hierarchy process (AHP), weighted linear combination (WLC), and fuzzy logic (FL). Eight articles use more than one method to create maps. The criteria used to create maps were divided into eight sectors: human, animal, animal reservoir, vector, ecological, physical, climate, and socioeconomic. The types of criteria used are human population, animal, and vector density; animal reservoir density and distribution; landcover and NDVI; distance and characteristics of water/soil; temperature, humidity, rainfall; knowledge, and Provincial Gross Domestic Product (PGDP).
AHP is the most used method when criteria from all eight groups are present, often in combination with FL and/or WLC. The data are collected with satellite, ground sensors, interviews, or field observations but they may also be density or socio-economic related. AHP is primarily used for satellite and density data, WLC just for density data, and FL for density data as well as for data collected with satellite, ground sensors, and field observations. Socio-economic data and the ones collected with interviews are not widely used. The most used methods for sensitivity analysis are enumeration and One-at-a-time. For validation, the most used method is field validation.
Identifying the most used MCDAs and the types of data required will allow us to use MCDAs properly. This method will help identify the drivers that most lead to zoonotic outbreaks to support planning, decision-making, and prioritizing areas for targeted prevention interventions
R01.1 The use of spatial metrics to select regions for enhanced bovine tuberculosis control measures in Ireland
No full textRegionalization, the use of stricter disease and wildlife management controls in certain areas, has been proposed as a method to bring about the eradication of Bovine Tuberculosis (bTB) in Ireland. We used spatial Information to compute metrics for the selection of counties within Ireland where such stricter controls could be applied. Counties were ranked based on cattle demographics, bTB prevalence, farm fragmentation, movement network measures, information on badger abundance and control, and the predicted impact of risk based trading on cattle sales.
Herd fragmentation was measured as the percentage of herds with land outside of the county and also the percentage of land fragments whose home farm was in another county. These two metrics closely matched each other for a given county, but they varied considerably between counties, from 1% to 24%. Movement network information was combined with a herd risk model based on network metrics, badger abundance and herd and environmental characteristics to evaluate the proportion of moves which would be deemed high-risk under a risk-based trading regime. This would result in up to 53% of a county’s cattle sales and 42% of its herds being affected. This was compared to a more conservative approach which may be more practical to implement, which was based purely on the test history of each herd. This second approach resulted in a much smaller number of trades being potentially restricted. Wildlife metrics related to badger control and vaccination also varied considerably between counties. We also used information on cattle market sales to calculate cattle market catchment areas, as an alternative to counties a candidates for regionalized control.
These results will be used by the Irish government to select candidate areas for stricter bTB control measures
P06.6 Spatial risk maps for African Swine Fever transmission in the Latvian wild boar population
No full textSpatial transmission kernel modelling is a powerful approach for modelling disease transmission risks between localized population units, such as livestock farms. In this study, we applied this modelling approach to African Swine Fever (ASF) transmission in Latvian wild boars, using spatial grid cells representing local wild boar habitat areas as population units. In particular, we consider the context of designing wild boar vaccination programs and use the risk maps to investigate the influence of vaccination coverage on transmission risk. Using transmission kernel estimates obtained from outbreak data in Estonia and wild boar hunting bag information from Latvia, we first calculated spatial transmission risk maps for ASF in Latvian wild boars. We expect comparing close-by countries the transmission kernel estimated for ASF transmission in Estonian wild boar will also be representative of the Latvian context. However, considering the occurrence of attenuated ASF virus in Latvia since 2017, we also expect that the Latvian outbreak data could as well be different from the one used for Estonia, particularly since the infection might have evolved and epidemic dynamics could be different from those observed 5 years ago. We used these risk maps to identify predicted high-risk areas of transmission and compared, retrospectively these to actual ASF transmission patterns observed in Latvia. Subsequently, we investigated how predicted transmission risks scale down for different wild boar vaccination coverage scenarios. We discuss the possible implications for the effectiveness of vaccination strategies in the Latvian wild boar population, considering both attenuated and acute ASF virus strains
R07.1 Earth Observation-based Self Organising Map for Northern Africa regions
No full textThe global climate is undergoing relentless change. While it is a complex phenomenon with difficult-to-predict full-scale impacts, its influence on the emergence of diseases, especially vector-borne diseases (VBDs), is widely acknowledged. Therefore, comprehending our landscape and examining its potential shifts over time due to alterations in climatic and environmental factors becomes crucial.In the framework of the WOAH project "Defining Ecoregions and Prototyping an EO-based Vector-borne Disease Surveillance System for North Africa (PROVNA)," we have identified ecoregions across North Africa, sharing similar environmental and climatic characteristics and which can serve as a pivotal foundation for enhancing surveillance systems and early warning system for vector-borne viruses. Selected EO data products for the period 2018-2022 (Land Surface Temperature Day and Night - LSTD and LSTN, Normalised Difference Vegetation Index - NDVI, Soil Moisture - SM, Normalised Difference Water Index - NDWI, Rainfall - RF) at 250 meters/16 days resolution have been collected, aggregated, and standardized at a season/year level. We used the (super)SOMs method (an unsupervised neural network clustering method) to get a topology-preserving map, transforming a complex high-dimensional input space into a simpler low-dimensional (typically two-dimensional) discrete output space. The resulting map is easy to interpret and can be used for classifying new (both in space and time) observations.A stratified random sampling, preserving the spatial and temporal variation of the environmental factors, was used to half the ~3 hundred million pixels of the entire extent and to create the dataset to train the SOMs. An RGB colorization was applied to the trained (super)SOM map (a hexagonal grid made up of 40x40 neurons for 20,000 epochs) to further improve its readability with respect to the input environmental variables averages (the red channel was assigned to LSTD and LSTN, the green channel to NDVI and NDWI and the blue channel to RF and SM). Finally, the affinity propagation clustering algorithm was applied to the map’s neurons (1.600) to group them and get the number of distinct ecoregions.The map clustering was in the end used to classify all of the pixels and to get yearly classification rasters.The proposed approach allows to apply the SOMs algorithm to EO data for a very large area, classifying landscape (and its temporal evolution) while preserving high spatial resolution and at the same time getting an immediate interpretation of similarity. This model will effectively support Competent Authorities in North Africa to identify locations where applying surveillance activities (while optimising financial, material and human resources) for identifying potential viruses in the area such as Rift Valley Fever which pose a threat for the entire Mediterranean region, including Europe
Salmonella enterica diversity and antimicrobial resistance profile in broiler slaughterhouse by-products
Get PDFThe aim of this study was to investigate the presence of Salmonella enterica in by‑products (feathers, spleen, cecum, and crop) from broiler slaughterhouses as well as to determine the antimicrobial resistance profile of the identified serovars. Forty‑four lots of broilers in nine slaughterhouses located in the central‑west region of Brazil were evaluated. Samples of spleen, feathers, cecum, and crop were collected in a pool and a total of 1,232 samples were evalueted. These were processed for conventional bacterial isolation and subjected to biochemical and serological tests to identify serovars. The identified serovars were subjected to the antimicrobial susceptibility test, where nine different antimycotics were investigated. Salmonella enterica was identified in 7.1% (87/1,232) of all evaluated samples, mostly in feathers (12.3%) and spleen (8.1%). The most frequent serovars were Schwarzengrund (29.9%), Agona (25.4%), Mbandaka (12.7%) and Anatum (8.1%). Nine serovars showed resistance to at least one antimicrobial, especially serovars Mbandaka, Infantis and Typhimurium. Amoxicillin and tetracycline were not effective in inhibiting at least five and four serovars, respectively