Veterinaria Italiana (Journal)
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O1-3 Canine Brucellosis in France due to Brucella canis: an emerging disease?
No full textCanine brucellosis due to Brucella canis is a neglected and underdiagnosed disease in many regions of the world. Currently, this disease is a main cause of abortions or infertility in dogs, or others symptoms like discospondilitys or subclinical forms. For the last two years, canine brucellosis reappeared in Europe and could become endemic. At least 8 cases have been confirmed in kennels from France, from dogsimported from Eastern Europe (Russia, Belarus and Romania) and the USA. Other outbreaks have been reported in United Kingdom, Germany, Italy, Portugal and the Netherlands or as sporadic cases in other countries. Serological, bacteriological and molecular biology approaches can be used and combined to identify or follow the infection. However, it is not possible to exclude the infection in case of negative results. The number of pets adopted in UK have largely increased during the COVID19 pandemic, especially new dogs breeds. This can be correlated with the emergence of canine brucellosis in European countries, as infected dogs have been mainly imported from foreign countries. Serological analyses are useful to identify and to follow the infection, due to the rough LPS avoiding the cross reaction with other smooth Brucella species. Bacteriological and molecular analyses have been also performed, as the isolation of strains allow genotyping and phylogenetic analyses. These analyses are important to track the source of infection and try to identify specific routes of transmission. This study presents an overview of canine brucellosis in France, with the combination of different approaches, allowing to hypothesize about introduction or emergence of the disease in the country
O3-5 Novel H-NS-like Protein MucR Coordinates Virulence Gene Expression During Host-Association in Brucella spp. Through Silencer/Counter-Silencer Interactions
No full textCorrect timing of virulence gene expression is critical for successful disease outcomes and the persistence of pathogens within the host environment. The global transcriptional silencer H-NS is a nucleoid-associated protein (NAP) that is important for coordination of virulence in many bacteria including Escherichia coli, Shigella, Salmonella, and Vibrio. In these bacteria, H-NS-mediated silencing is overcome through direct antagonization via transcriptional counter-silencers that bind to gene promoter regions, displace H-NS, and permit transcriptional activation. Brucella spp. and related members of α-proteobacteria lack functional H-NS homologs, so it is unclear whether other proteins are involved in performing analogous functions during host-association and pathogenesis. We have identified the Zn finger protein MucR as a novel H-NS-like protein that is critical for virulence in Brucella spp. by binding to and directly repressing virulence gene promoters in an H- NS-like manner. We show that MucR specifically interacts with AT-rich DNA regions containing multiple TA steps. Building on previous work, we show that oligomerization is required for proper MucR activity. Further, we demonstrate the stress- responsive regulator and SlyA-homolog, MdrA, acts as a direct counter-silencer to MucR through competition on virulence gene promoters. Consistent with the role of MucR as an H-NS-like protein, hns from E. coli is able to functionally complement mucR mutants in Brucella spp. Together these data demonstrate the role of MucR as a novel H-NS-like protein and highlight the importance of silencer/counter-silencer interactions in the pathogenesis of Brucella spp. and related bacteria
O8-2 Follow up investigations on non-infected dogs adopted from the B. canis outbreak
No full textCanine brucellosis due to Brucella canis is a contagious disease characterized by abortions in females and epididymitis, testicular atrophy, prostatitis and infertility in males. In April 2020 a B. canis outbreak was notified for the first time in Italy. The infection occurred in a breeding kennel in central Italy that was hosting mostly chihuahua but also other toy breed and involved more than 600 animals. After confirmation of infection, a ban for animal movement and selling was applied. A no-kill strategy was implemented for outbreak management and a procedure was developed to identify B. canis non-infected dogs subsequently given for adoption, this in order to reduce kennel population. This study describes the procedure applied to select B. canis non-infected animals eligible for adoption and report results of follow-up laboratory investigation carried out on adopted animals finalised to exclude any late occurrence of infection. To reduce the dog population hosted in the infected kennel a protocol was developed that combined neutering and B. canis serological testing. Negative animals were transferred to a buffer kennel for a second round serological and bacteriological testing. Negative animals were considered eligible for adoption. Following adoption, a procedure for tracing and monitoring rehomed dogs was developed and is unde implementation. More than 300 animals were identified as B. canis non-infected and rehomed. To date, over 12 months since first adoption, none of the rehomed animals developed infection. Our results suggests that whenever applicable, rehoming of B. canis negative animals should be considered and included in the toolkit for management of B. canis outbreaks
P8-13 Changes of laboratory findings in two dogs infected with Brucella canis following antibiotic treatment and orchiectomy: a case report
No full textCanine brucellosis caused by Brucella canis (B. canis) is an emerging infection affecting dogs worldwide and potentially transmissible to humans. The disease is frequently observed in stray dogs or in breeding kennels where is responsible for important economic losses. Disease control measures consider treatment or euthanasia of infected animals, as no vaccines are available. Use of antibiotics is not encouraged due to the uncertain success of the cure and the high risk of disease relapse. Still this represents the only alternative to euthanasia, usually combined to orchiectomy and ovary-hysterectomy to reduce the risk of disease transmission. Data on the combined effect of castration and antibiotic treatments of B. canis infected dogs are limited and very often follow up information are missing. The aim of the study was to describe from a diagnostic laboratory perspective the effect of antibiotic therapy and castration on male infected dogs. Twomale dogs of 8 months and 6 years old were identified as B. canis infected during trace back activities related to the B. canis outbreak occurred in Italy in 2020. One animal derived from the infected breeding kennel (patient 1) and was showing cryptorchidism while the second animal (patient 2) was exposed to direct contact with the infected dog, sharing the same environment. Laboratory investigations for B. canis were carried out before and after antibiotic treatment and orchiectomy, on sera, EDTA blood, urine and testicles. Animals were treated with two different therapeutic protocols. Before treatment, both animals showed high level of antibodies to microplate agglutination test and B. canis was also isolated from blood and urine. One month after antibiotic treatment, we observed a decrease of antibody titers and just for patient 1, B. canis was detected only by PCR from blood and urine. Orchiectomy was executed after one moths of antibiotic therapy and and no bacteria were detected in the testicles. Animal tested negative to both serology and bacteriology at follow up analyses carried out 1 year later. Despite the encouraging results, periodic follow up remain mandatory to exclude possible relapses of infection. Data also demonstrated that antimicrobial treatment influences laboratory test outcome
K6 The diagnosis of brucellosis
No full textThe diagnosis of brucellosis encompasses multiple scenarios, methodologies, requirements, objectives, hosts and pathogen species. Definitive diagnosis is only possible via isolation and identification of Brucella but this presents challenges due to imperfect sensitivity, biorisk, capability and cost. Other approaches are needed to navigate these issues including measurement of the host immune response and detection of DNA. Alongside epidemiological information the results from such tests can inform a suitable risk-based diagnostic interpretation and response. Despite the challenges of brucellosis control the diagnostic element is in many cases strong. For example, the Brucella sLPS is a gift to the diagnostician. It is abundant on the cell surface, is robust, multivalent, amphiphilic, T-independent and subject to the adaptive immune response and a single type may be universally applied for detection of infection with [nearly] all smooth stains. It underpins all primary serological assays, the appropriate implementation and interpretation of which should be a primary objective of any testing system. Diagnostic weaknesses include the non-universal attainment of good quality diagnostic antigen, an issue compounded by costs and the biological risks. Induction of false positive serological reactions due to vaccination or infection with cross reactive organisms occur - althoughtheir occurrence and significance varies considerably by circumstance. Measurement of the cellular immune response may assist diagnosis but access to the required reagents is poor. At the molecular level, recombinant and synthetic approaches offer some basis for insight and solution. Recombinant skin test antigen is being developed for bovine tuberculosis. Data using synthetic OPS based oligosaccharides suggests that antigen presentation impacts upon antibody induction - information which may provide new options for resolution of FPSRs. An alternative strategy is the application of other abundant cell surface antigens. The atypical properties of the rLPS core make this an attractive target. Select excipients appear to improve its diagnostic potential with respect to infection with smooth and with rough strains. At the global level the prevalence of disease in humans and animals remains poorly understood. Are limitations in existing diagnostic tools contributory to this knowledge gap? If so, what more can be done and how can this be achieved
R08.4 Development and application of a network model to understand the epidemiological and economic burden of an aquaculture disease outbreak under different control strategies
No full textAquatic animal diseases cause high mortality, threatening biodiversity, and food security, resulting in substantial economic impact to aquaculture and recreational fishing industries. Tools such as mathematical models and computer simulations are valuable for predicting the potential spread and impact of disease, thereby informing evidence-based, cost-effective management policy and decision making.
The AquaNet-Mod is a modelling tool used to simulate the spread of disease across a network of connected sites representing the aquaculture industry, in this case the trout aquaculture and inland fishery industry in England and Wales. Each site is considered a single epidemiological unit, and disease can spread between sites via four transmission mechanisms: live fish movements, river-based transmission, short distance mechanical transmission and distance independent mechanical transmission. The connectivity between sites is based upon real-world data gathered as part of the Competent Authority’s statutory monitoring. Following the seeding of infection, sites transit between three disease states: susceptible, clinically infected and sub clinically infected, according to defined criteria. Disease spread can be interrupted by the application of disease mitigation measures and controls such as contact tracing, culling, fallowing and surveillance. The model also incorporates economic costs to affected sites and the Competent Authority associated with infection and controls, allowing the cost of an outbreak to be estimated and compared between control scenarios.
Here we present AquaNet-Mod outputs for Viral Haemorrhagic Septicaemia (VHS), a freshwater salmonid disease listed by WOAH and in the UK. Simulations showed that, intuitively, controls which reduced the total number of infected sites reduced the overall epidemic costs. In particular, the model demonstrated that the current disease controls in England and Wales resulted in the lowest total number of infected sites, on average, and that contact tracing was both effective at reducing the spread of disease through the network and relatively low cost. The merit of this model for evaluation of disease spread and the cost-effectiveness of controls, in the context of policy, is discussed.
R08.3 On the interaction between bovine and bubaline trade network in epidemic spreading
No full textThe Italian cattle movement network has been extensively studied in the literature (Bajardi et al., 2011) while the Italian buffalo network has not been investigated. Throughout the peninsula there are several farms that breed buffaloes and more than 60% of these also breed cattle. Such situation highlights potential transmission of pathogens between the two species. In order to study the interaction between the two networks we considered a pathogen, Q-Fever, that can infect both species and also humans. It is a dangerous disease for humans, but, since it is usually asymptomatic in animals, it is often neglected. This pathogen is widespread among cattle and buffaloes in Campania region (Ferrara et al., 2022).
We want to study if the buffalo network can support the diffusion of the pathogen and spread it to the cattle network through those farms that breed both species. To do this, we consider a SIS model. Since the diagnosis is complicated and usually carried out at herd level, the epidemiological units of our model are the farms. An infectious farm is a farm where at least one animal is infectious.
A susceptible farm that receives animals from an infectious farm will also become infectious according to a defined probability of infection.
We randomly infect a small subset of the buffalo farms (0.05% of all the farms), and then simulate the diffusion of the pathogen using real movement data between 2017 and 2020, aggregated by week. Given the above considerations, we considered a low probability of “healing” among the farms, 0.1%.
In Table 1 (https://www.veterinariaitaliana.izs.it/index.php/GEOVET23/article/view/3247/1405) we can observe the results of 100 different simulations for different infection probabilities.
From this analysis, it can be concluded that the interaction between the buffalo and the cattle network is not negligible. Indeed, the buffalo network may support the spread of a pathogen and is able to spread it within the cattle network through those farms that breed both species. Further work could include a more in-depth study of both the buffalo network and the interaction between the two species in the multidimensional network. In addition, improving the model by considering surveillance in the cattle network might be interesting to observe the impact that the buffalo network would have by itself
R03.3 Predicting socioeconomic and intervention-related factors influencing the risk of vector-borne diseases using Demographic and Health Surveys; a case study of malaria
No full textVector-borne diseases (VBDs) account for more than 17% of all infectious diseases, with an annual global burden of more than 700,000 deaths. In this context, disease risk maps are a key decision-making tool to target control interventions. Yet, these maps are often not sufficiently accurate, as they rarely capture all disease determinants. They usually rely on environmental factors that influence vector presence and vectorial capacity, e.g. climatic and land cover variables. However, socioeconomic factors, such as housing quality or education, and human behaviors, such as the use of preventive measures, are also known risk factors. Environmental and socioeconomic determinants are still rarely combined in prediction models. While environmental factors are commonly derived from satellite-based earth observation, socioeconomic factors are measured through household surveys, which are more time-consuming and challenging to collect. Demographic and Health Surveys (DHS) provide estimates of key demographic and health variables based on nationally representative samples. These include indicators of respondents’ socioeconomic level (e.g. wealth index and education) and use of preventive measures for malaria (e.g. bed nets and indoor spraying). Geolocations for survey clusters are available, and these can be interpolated to create grids that can be further used in spatial models of VBDs. The production of interpolated surfaces of DHS indicators has been widely studied. However, studies do not reach a consensus on the modelling workflow to be used and different types of models have been implemented with different levels of complexity and information required as input. Overall, three types of approaches have been used: (1) spatial interpolation methods, (2) ensemble methods, and (3) Bayesian models. In this research, we focus on malaria as an example of VBDs, and we aim at comparing several methods for predicting DHS indicators influencing malaria risk.
We selected DHS indicators falling into two categories: socioeconomic variables and malaria preventive measures. Using kriging, random forest modelling and spatial Bayesian models (INLA-SPDE implementation), we modelled and predicted these indicators at a high spatial resolution across several sub-Saharan African countries. A set of predictors representing climate, land use and land cover was compiled for use in some of the models.
Different categories of DHS indicators required different modelling approaches; indicators of malaria prevention were best modelled by capturing the spatial autocorrelation pattern, while socioeconomic variables were best predicted with spatial predictors.
These findings highlight the need to test different modelling approaches when mapping human determinants of VBDs risk. Ultimately, these factors can be integrated into a single modelling framework with environmental factors to map malaria risk and identify key drivers. As DHS are conducted consistently in many countries, the methods used in this research are applicable beyond sub-Saharan Africa and could be replicated for other infectious diseases.
R06.3 Spatially explicit agent-based modeling as a tool in aiding African swine fever mitigation and eradication in Thailand
No full textAfrican Swine Fever (ASF) is a highly contagious viral disease of swine with exceptionally high mortality in domestic pigs. Due to the lack of effective vaccine and treatment at present, the socioeconomic impact losses caused by the virus to the swine industry across the globe is very high (Blome et al., 2020). Many studies have shown that disease modeling and forecasting can provide valuable information on the overall disease impact and can be used as guideline for decision makers to evaluate their disease surveillance and control programs (Beaunée et al., 2023; Mur et al., 2018). With the recent arrival of ASF in January 2022 in Thailand, a spatially explicit agent-based model has been constructed to support the surveillance and control strategies for ASF within the country.
In this study, we used a fine scale grid with detailed pig demographic and pig movement data in Thailand to evaluate the spatio-temporal dynamics of ASF at local and national level. Our model aimed to estimate the impact of the disease under diverse interventions such as stamping out, movement restrictions, compartmentalization, and early detection. We estimated the total number of infected farms, number of infected areas and duration of the outbreak under diverse epidemiological scenarios.
Our model estimates that reducing the average days to detection from 21 to 14 days will reduce the overall epidemic impact by 43%. Our results provide high resolution information about the regions with higher impact and the complex disease spread networks developed. These results will be used to support and improve the current ASF eradication efforts in Thailand and can be easily adapted to other transboundary animal diseases or regions under different epidemiological settings
P08.2 Analyzing the movement network of swine within and between states in the United States
No full textMost preventative and countermeasure tactics aimed at controlling between-premises disease spread in livestock systems rely on identifying and restricting animal movement. Therefore, it is essential to uncover between-premise movement dynamics, including nationwide network profiles and distances to which animals are transported, to develop network-based control strategies. Currently, within-state animal movements in the U.S. are not regulated, making it difficult to acquire accurate data necessary to describe animal movement patters at large-scale. Here, we present an approach which combines network and spatial analysis using large-scale data of six swine production companies is the US. We analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3,973 premises, and 391,625,374 pigs transported across 22 U.S. states. We calculated premises-to-premises movement distances and identified the main transportation routes used by each company by considering origin and destination geolocations. Furthermore, we constructed an unweighted directed temporal farm contact network at 180-day intervals to calculate farm degree loyalty. With this information, we developed an out-going farm, temporal contact chains model to obtain infection chain distributions. Model outputs were used to identify hubs in the network which can aid in developing targeted control actions via node removal over the network, and compare multiple scenarios (e.g., 5%, 10%, 15%, 20%, 25% of the farms are removed). Node removal is carried out by considering the network metrics degree, betweenness, and cluster coefficient. Our results showed that the median distance between pig premises movements was 74.37 km, with a median of 52.71 km and 328.76 km for interstate movements. On average, 2,842 premises were connected via 6,705 edges. The premises-level network exhibited a loyalty, with a median of 0.65 (IQR: 0.45 – 0.77). When targeting 25% of the premises based on degree and betweenness, we reduced the spread to 1.23% and 1.7% of infected premises, respectively. While a complete shipment movement record for the entire U.S. does not exist, our results demonstrated the value of multi-state movement data to enhance the development of outbreak mitigation tactics