Veterinaria Italiana (Journal)
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R05.3 How the scale of temperature measures affects the estimate of transmission of mosquito-borne diseases
No full textVector-borne diseases account for more than 17% of all human infectious diseases and result in more than 700,000 deaths annually. A significant part of vector-borne diseases is caused by pathogens transmitted by mosquitoes, and include diseases as relevant as Malaria, Dengue, or West Nile Fever.
Mosquitoes are ectotherms; therefore, their physiology and life histories are driven by environmental temperature (Mordecai et al., 2013). Similarly, the processes involved in the transmission of mosquito-borne pathogens are highly dependent on ambient temperatures (Shocket et al., 2018). The relationship between those factors and the temperature is usually unimodal (i.e., once the minimum temperature needed for that factor is reached, the value of the factor increases until it peaks at an optimum temperature, and then decreases to zero at the maximum temperature). The transmission of mosquito-borne diseases can be quantified using the Basic Reproductive Number (R0), which defines the number of cases of a disease that arise when one case is introduced into a totally susceptible population. An ideal approach to evaluate the relationship between the risk of transmission of mosquito-borne diseases and temperature is the R0 equation.
The objective of our study was to quantify the effect that the precision with which temperature is measured (hours, days, months) has on the estimates of transmission of mosquito-borne diseases. In order to do that, we used mechanistic models to quantify the basic reproductive number (R0), which we applied to different mosquito-pathogen combinations: West Nile virus (WNV) and Culex pipiens, dengue virus (DENV) and both Aedes albopictus and Aedes aegypti and P. falciparum malaria parasites and Anopheles mosquitoes. At the world level, the higher the precision, resulted in significantly smaller estimates of transmission and much larger transmission areas, and those results were consistent for different mosquito-pathogen combinations and for the different months of the year. However, when that effect was evaluated at a smaller spatial scale (i.e. climatic areas), spatio-temporal heterogeneities were observed. For example, hourly rather than monthly temperatures tended to decrease the estimates of transmission in areas of tropical climate but increased them in areas of continental climate. While those effects were clearly different between climatic areas, they were consistent for the different mosquito-pathogen combinations within a climatic area
S06 Choice of Landscape Discretisation Affects the Rate of Spread in Wildlife Disease Models
No full textResearch on the interplay between domestic animal and wildlife populations has gained momentum recently. African Swine Fever (ASF) is a highly infectious viral disease that is of particular interest due to ongoing outbreaks in Europe, where wild boar constitute a reservoir of disease spillover into domestic pigs.
It is theoretically desirable to model spread of the ASF virus through a wild boar population using a continuous-space model, where wild boar distribution is inferred from spatial mapping of the available habitat. Because continuous-space models are difficult to work with, these are typically approximated using discrete-space models, where the wild boar population is divided into patches within the landscape.
In this study, we investigate the epidemiological impact of different assumptions regarding the spatial discretisation of a wild boar population. We use a simulation study with a fixed population of wild boar within a fictional, homogeneous landscape. This landscape is discretised into patches using different methods. A deterministic compartmental Susceptible-Infected-Recovered (SIR) model is formulated to allow pairwise spread between each patch depending on a transmission matrix β, which is then fit using an ODE solver. The scenario is as follows: Two buffer zones are created with a large gap in between. An outbreak is started in one, and the time until the other buffer zone has crossed a certain prevalence threshold (τ) is recorded. This process is repeated on the same landscape, but with different discretisations of the landscape at different spatial resolutions. For each simulation, transmission between patches is controlled by a pairwise transmission matrix β.
We considered many correction factors for β that were hypothesised to re-scale overall transmission so that τ is independent of landscape discretisation choice. These included: scaling with respect to the diameter of the patch, exponential distance decay, normalising discrete distances, and normalising using an integrated distance kernel. These corrections mask the inconsistency for some scenarios, but overall τ remains highly dependent on the choice of discretisation, suggesting that the underlying epidemiological process is impacted by the spatial discretisation. We also present an alternative method of formulating β, using pairwise proximity of artificially-created high-resolution lattice points that approximates transmission in continuous space. This yields a τ that is more robust to the details of landscape discretisation, so that the overall model results are less dependent on arbitrary decisions regarding size and shape of patches.
In conclusion, we propose that the method of landscape discretisation should be regarded as a source of uncertainty that impacts disease spread, and as such should be treated as a hyper-parameter in epidemiological models involving wildlife. It is important to ensure that overall conclusions from such models are robust to landscape discretisation choice
P04.5 Combining biosecurity and spatial information to support decision-making in fresh-water fish farms
No full textAccording to the new Animal Health Law, the surveillance scheme in fish farms, must consider the health status of the farms and the level of risk of introduction and spread of diseases. The classification of the various risk levels requires heterogeneous information including: spatial location, extent and connection to the hydrological network, and the biosecurity measures implemented. Hereby it is presented a method to rank the farms on the basis of their biosecurity, and integrate this information within the hydrological network, with the ultimate aim of informing the local health authority (LHA) of which farms are exposed to greater risk of transmission, once a disease has been detected.
Sixty-two salmonid farms located in the Autonomous Province of Trento, northeastern Italy, were included in the study. Biosecurity information was collected for each farms through checklists already provided in the National Legislation implementing the EU Directive 2006/88/CE. Although the checklist covered several topics, only information on biosecurity were considered in the study. The resulting 39 biosecurity items were scored via expert elicitation, allowing evaluating the importance of single item, while also accounting for the uncertainty potentially related to each of the 10 experts. Finally, to each farm a score derived by the presence/absence of those 39 items was assigned, permitting to rank the premises accordingly to their biosecurity. Data manipulation and elaborations were performed using the statistical software R. The hydrologic network was implemented, and fully validated, within a geo-database using PostGreSQL with PostGIS and PgRouting extensions. Location of farms and their productive data were acquired from the National Livestock Database. The location of connections between farms and rivers, likely water sources, and other points of interest were georeferenced and validated by veterinarians from the LHA. Biosecurity scores were combined with spatial information of the farms to produce thematic maps. Finally, a set of specific Geographic Information System (GIS) tools were implemented to support LHA and decision makers, combining spatial operations and biosecurity scores.
Overall the fish farming sector resulted having high biosecurity scores, with only a farms showing suboptimal of poor conditions (N=2/62, 3.23%). In addition, GIS-oriented functionalities were developed for: (i) spatial operations that consider the digital terrain model (e.g. buffers accounting for the slope); (ii) network operations for selecting farm (e.g.: upstream/downstream) and interrogating their biosecurity, (iii) rapid extraction of territorial information near the farms.
The study represent a starting point for creating of a GIS-based risk assessment tool, for identifying farms at risk of disease transmission, increasing the effectiveness of surveillance and disease response measures. Furthermore, the development dedicated GIS features might be exploited to create informative maps to support decision-making.
This work is funded by Italian Ministry in the National Research IZSVE 12-20 RC “AeGIS”
R04.5 Remote sensing as an additional tool for modelling spatiotemporal dynamics of questing Ixodes ricinus on Italian northwestern Alps
No full textThe distribution range of the tick Ixodes ricinus in Europe is expanding in latitude and altitude. Consistently, a rise in human cases of tick-borne diseases has been reported. Environmental and land-use changes, like the increasing abundance of wildlife and the abandonment of marginal territories, as well as the attenuation of winter temperatures and narrowing of the temperature range at high altitudes, led to the infestation of mountain areas previously not infested. In the Italian North-Western Alps, I. ricinus presence has been recorded above 1600m of altitude, and its abundance was related to altitudinal range and forest type. This study aims to re-evaluate previous records about questing I. ricinus nymphs distribution in relation with climatic and environmental factors.
Data from a previous study were employed (Garcia-Vozmediano et al., 2020). In the Susa valley, which stretches eastward for 80km from the French border to the city of Turin in the northwestern Italian Alps, 44 sites were selected. To collect ticks, from 2016 to 2019, the sites were sampled by dragging almost six times per year, from April to November. At sampling, temperature, relative humidity, weather condition and the presence of wildlife’s traces were recorded along with exact geographic location and habitat characteristics. In addition to the five predictors previously measured on-site, we added ten measured from remote sensing. From digital terrain model, altitude, slope, and aspect were obtained. The Normalized Difference Vegetation Index (NDVI), which estimates vegetation coverage, and global horizontal irradiation, the sum of direct and diffuse solar irradiation, were included too. Weather conditions (minimum and maximum temperatures, saturation deficit, wind, and rainfall) were evaluated based on the I. ricinus survival range (Perret et al., 2000) and measured in the variable time spans preceding the sampling.
A spatio-temporal Bayesian LASSO model was implemented to select most relevant variables to explain I. ricinus nymphs’ distribution. Therefore, the model was fitted on a subset of 724 transects that ensured the most complete set of samplings on a subset of 29 sites.
Habitat, altitude, and wildlife were previously reported as key factors affecting ticks’ density, but their relevance decreased when other variables were considered. Relative humidity and saturation deficit measured at sampling showed a non-linear effect on ticks’ distribution but remained valid predictors. In addition, our model showed that nymphs’ density is related to the global solar irradiation and adverse weather conditions during the days preceding the sampling.
The recent infestation of I. ricinus of high Alpine areas requires additional efforts to understand the complex adaptation mechanisms of this species to the environment. This study helps in the comprehension of the role of environmental and climatic factors and demonstrate how remote sensing variables can integrate data measured on-site
R06.1 Examining the Impact of Fencing on the Front Wave of African Swine Fever in the Korean Wild Boar Population
No full textAfrican Swine Fever (ASF) is a viral disease that affects both wild boar and domestic pigs. In recent years, this virus has spread rapidly across Europe and Asia, causing extensive damage to pig populations and related industries. In South Korea, the first notification in wild boar was made in October 2019 in the northern region bordering North Korea. Since then, the virus spread southward, leading to more than 2,800 reported cases in wild boar and sporadic outbreaks in domestic pig farms. To mitigate its spatial spread, Korean authorities built fences over 2,300 km. In this study, we aimed at evaluating the effectiveness and its heterogeneity of these fences in controlling the spread of ASF in South Korea, using the spatiotemporal distribution of ASF cases reported in wild boar between November 2019 – March 2023 in South Korea.
We first identified the main spatiotemporal clusters and selected only the front wave cases within each cluster. By examining these front wave cases, we estimated the spread rate (in km²/week) using trend surface analysis (Tisseuil et al., 2016). Finally, to evaluate the effectiveness of fencing on the spread of ASF, we created a null scenario by randomly redistributing front wave case locations as described in Dellicour et al. (2020). By comparing the observed patterns to the expected patterns from the null scenario, we were able to test whether fences effectively reduced the spatial propagation of the virus as well as the front wave velocity.
Among the total 2,661 wild boar cases reported in the study period, we identified nine clusters that included 691 front wave cases. We restricted our analysis to the 619 front wave cases from the four largest clusters. Overall, the front wave spread by 0.52km2 per week at national level, with heterogeneous velocities calculated in each cluster, from 0.15 km2/week to 0.99 km2/week. For two out of the four clusters studied, we found that the number of fence-crossing events was statistically significantly lower than under the null scenario, suggesting that fences reduced the propagation of the virus across the fences in these regions. However, we did not find any evidence for the two other clusters. Also, we did not find any statistical evidence suggesting that fences slowed down the front wave velocity.
We conclude that ASF virus in South Korea spread geographically at a faster rate than in other environmental contexts like Belgium (Dellicour et al., 2020) and that this geographical spread must be associated to specific determinants that are yet to be elucidated. The heterogeneous effects of fences can be explained by their spatio-temporal erecting patterns and environmental determinants. We highlight the crucial need to account for unobserved ASF virus diffusion when deciding where and when to place them
P02.7 Real-time monitoring and forecasting of Rift Valley fever in Africa to drive preparedness and anticipatory actions
No full textRift Valley fever (RVF) is a vector-borne disease that has severe impacts on livelihoods, national and international markets, and human health. RVF is currently limited to Africa and parts of the Near East with the potential to expand globally. In livestock, the disease affects sheep, goats, cattle, buffaloes, and camels. Outbreaks are closely associated with climate anomalies (e.g., periods of heavy rains and prolonged flooding), which increase habitat suitability for vector populations, influencing the risk of disease emergence, transmission and spread. Early warning systems represent an essential tool to enable national authorities to implement measures preventing outbreaks. In this context, the Food and Agriculture Organization of the United Nations (FAO) has developed a web-based RVF Early Warning Decision Support Tool (RVF DST), which integrates near real-time RVF risk maps with geospatial data, RVF historical and current disease events from EMPRES Global Animal Disease Information System (EMPRES-i) and expert knowledge on eco-epidemiology. This tool has been crucial in successfully forecasting hotspots for RVF vector amplification, as it provides recommendations and early warning messages for countries at risk of RVF outbreaks. The tool is used to build capacity for early warning and forecasting at country level, and demonstrates how near real-time modelling, risk forecasting and digital innovation can enhance preparedness and anticipatory actions
R10.2 Estimating the coverage of slaughterhouse surveillance for bovine tuberculosis
No full textSlaughterhouse surveillance plays a key role in disease detection, especially for zoonotic diseases such as Bovine Tuberculosis (bTB). Premises are under slaughterhouse surveillance if a sufficient number of their animals reach slaughterhouses in order to detect an outbreak. Since the probability of detecting an outbreak is dependent on the number of animals sent to slaughterhouses, total number of animals in the farm, and the prevalence of disease, premises that send animals directly to abattoirs might not be under surveillance, while premises that do not send animals directly to abattoirs might be (through indirect contacts). Therefore, social network analysis is essential to estimate the coverage of one of the main strategies for the surveillance of bTB and could help identify the premises and/or regions that are within the reach of this strategy and those that are not. This study aimed to evaluate the coverage of the Bovine Tuberculosis slaughterhouse surveillance in a state of Brazil. We used a two-year animal movement database, hypergeometric distribution, social network analysis, and stochastic simulations to estimate the probability that at least one infected animal of a premises would reach a slaughterhouse. We considered that premises are under slaughterhouse surveillance if this probability is at least 80%. Results showed that 45% of premises and 86.5% of cattle were covered by the slaughterhouse surveillance in the state. These results can be used to identify regions and premises that are not within the reach of this strategy, prompting stakeholders to develop other surveillance components to fill the gap. Results could also be integrated with the probability of detecting bTB lesions in slaughterhouses to estimate the sensitivity of a slaughterhouse surveillance system. This approach can also be adapted to estimate the coverage of slaughterhouse surveillance for other diseases
P10.11 Incidence rates of tumors in dogs (Campania Region – 2018/2022)
No full textKnowing the health state of synanthropic animals has a high sanitary importance: it acts as a mirror for human health state as dogs share the same enviroment. Animal populations spontaneously exposed to contaminants and mutagens can represent sentinel of environmental risk factors, recognizing in them a sentinel role. The Animal Cancer Registry (RTA) is essential for the collection and comparison of data related to human/animal neoplastic pathology. Animal sentinels develope cancer erlier than man Kind: this allows the development of risk maps, reducing the time for preventive action.
The aim of the work is to evaluate if there is a difference between the incidence rates of tumors in dogs of Salerno province and in dogs of Naples province ; this evaluation could be use for in order to focus activities to safeguard Human Health.
Data of Campania RTA (provinces of Salerno and Naples in the years 2018-2022) for the canine species have been analysed. Data were extracted from the BDU (regional database) and from the Laboratory Management System of the Experimental Zooprophylactic Institute of Southern Italy. The statistical analysis was carried out through the software R version 4.1.0.
The dataset consists of 493 cases in dogs. For the provinces analysed, four age groups were identified; subsequently, a direct standardization was performed to compare the two incidence rates by eliminating the effect of age as a confounder.
The crude incidence rate for the province of Naples is 6.20 while for the province of Salerno it is 5.82: Naples shows a higher incidence rate than Salerno. Therefore with direct standardization we obtain that for the two provinces the rates are similar: the incidence rate of tumors in dogs in Salerno is 0.26 while in Naples it is 0.24
Cephalosporin susceptibility of Staphylococcus aureus strains isolated from commercial rabbit and goats farms in Spain
No full textAntimicrobial drug resistance is an important problem that challenges veterinary clinicians to provide effective treatments without further spreading resistance to other animals and people. The most commonly used pharmacodynamic parameter to define potency of antimicrobial drugs is minimum inhibitory concentration (MIC). The aim of this study was to evaluate the antibiotic susceptibility of thirty-six strains of Staphylococcus aureus isolated from dairy goats with mastitis and rabbits with chronic staphylococcosis. Four cephalosporins were tested: cephalexin, cephalotin, cefonicid and ceftiofur. MIC tests were performed according to the microdilution broth method. The calculated values of sensitivity in goats and rabbits were 66.67% and 72.22% for cephalexin, 72.22 % and 94.44% for cefonicid, 77.78% and 94.44% for cephalotin and 77.78% and 100% for ceftiofur, respectively. For all antibiotics, MIC90 of S. aureus from rabbits were lower than MIC90 from goats. These data suggest that more antibiotics are used in goat milk production than in rabbit farming. According to MIC values obtained in this study, ceftiofur and cephalotin may be the best option for treating S. aureus infections in lactating goats. For rabbits, ceftiofur showed lowest MIC values, therefore, it could be an alternative to treatment the infections caused by S. aureus in this species. 
Seroprevalence of Bovine ephemeral fever virus in Gujarat State of India
Get PDFBovine ephemeral fever (BEF) virus (BEFV) is an arthropod borne virus that causes bovine ephemeral fever or three‑day sickness in cattle and buffaloes. This is the first report on seroprevalence of BEF in cattle and buffaloes in Gujarat, India. Total of 92 animals, 78 cattle and 14 buffaloes from three regions (districts) of Gujarat state of India, were screened for the presence of anti‑BEF antibodies. A total of 27 out of 92 animals were found positive and overall seroprevalence detected was 29.34% (95% CI 20.0‑38.6%). A total of 19 out of 78 cattle and 8 out of 14 buffalo’s samples were found positive BEFV antibodies. Species‑wise seroprevalence in cattle and buffaloes was 24.35% (95% CI 14.8‑33.8%) and 57.1% (95% CI 31.2‑83.0%), respectively. There was a statistically significant (p < 0.05) species effect based on the seroprevalence. In cattle, location‑wise seroprevalence was observed to be 26.82% (95% CI 13.2‑40.3%) and 21.62% (95% CI 8.3‑34.8%) in Navsari and Banaskantha districts, respectively. The effect of location is not statistically significant (p < 0.05). Cytopathic effect of Vero cells was characterized by rounding, granulation of the cytoplasm within 48‑72 hrs of post infection. This was the first report demonstrating the presence of BEFV in Gujarat state