The recent occurrence and spread of African swine fever (ASF) in Eastern Europe is perceived as a serious risk for the pig industry in the European Union (EU). In order to estimate the potential risk of ASF virus (ASFV) entering the EU, several pathways of introduction were previously assessed separately. The present work aimed to integrate five of these assessments (legal imports of pigs, legal imports of products, illegal imports of products, fomites associated with transport and wild boar movements) into a modular tool that facilitates the visualization and comprehension of the relative risk of ASFV introduction into the EU by each analyzed pathway. The framework's results indicate that 48% of EU countries are at relatively high risk (risk score 4 or 5 out of 5) for ASFV entry for at least one analyzed pathway. Four of these countries obtained the maximum risk score for one pathway: Bulgaria for legally imported products during the high risk period (HRP); Finland for wild boar; Slovenia and Sweden for legally imported pigs during the HRP. Distribution of risk considerably differed from one pathway to another; for some pathways, the risk was concentrated in a few countries (e.g., transport fomites), whereas other pathways incurred a high risk for 4 or 5 countries (legal pigs, illegal imports and wild boar). The modular framework, developed to estimate the risk of ASFV entry into the EU, is available in a public domain, and is a transparent, easy-to-interpret tool that can be updated and adapted if required. The model's results determine the EU countries at higher risk for each ASFV introduction route, and provide a useful basis to develop a global coordinated program to improve ASFV prevention in the EU.
Event Abstract Back to Event O, Salmonella, Where Art Thou? Modelling Salmonella infection in swine farms in Spain using Hamiltonian Monte Carlo methods Kendy Tzu-Yun Teng1*, Marta Martinez Aviles2, Maria Ugarte1, Carmen Barcena1, Ana De La Torre2, Gema Lopez3 and Julio Alvarez1, 4 1 VISAVET Health Surveillance Centre (UCM), Spain 2 Center for Animal Health Research, National Institute of Agricultural and Food Research and Technology, Spain 3 Ministerio de Agricultura, Alimentación y Medio Ambiente (Spain), Spain 4 Department of Animal Health, Faculty of Veterinary Medicine, Complutense University of Madrid, Spain Background Salmonella infection is the second most prevalent foodborne zoonosis in the European Union (EU) with 91,662 confirmed human salmonellosis cases in the EU in 2017 [1]. In Spain, pork and pork products are a major source of human salmonellosis [2]. Two of the most common Salmonella serotypes in swine, S. Typhimurium and its monophasic variant (I 4,[5],12:i:-), were shown to be the dominant serotypes associated with human salmonellosis in Spain [1]. EFSA baseline reports on Salmonella infection in swine in Europe demonstrated that Spain had one of the highest levels of infection in fattening and breeder pigs [3, 4]. Spatial modelling techniques have been widely applied to understand the epidemiology of diseases; they can help to detect areas of higher risk of infection/disease, which could then be linked to potential risk factors. Conditional autoregressive (CAR) models that allow considering explicitly second order spatial effects have been widely used for this purpose. These models can be fitted in a Bayesian framework using Markov Chain Monte Carlo Gibbs sampling through popular software such as WinBUGS and OpenBUGS, although, depending on the model structure, the convergence of certain models can be challenging. The current study explored the application of Stan, a Hamiltonian Monte Carlo-based framework, to fit Bayesian generalised linear regression CAR models using surveillance data of Salmonella infection at the pig-farm level in Spain. Stan is a state-of-the-art platform for full Bayesian statistical modelling [5]. It allows exceedingly flexible modelling with high-performance computation and has a highly supportive community that ensures information transparency. In this study, we first explored the spatial distribution and potential spatial trends in Salmonella infection at the pig-farm level in Spain by using multiple spatial analytical techniques and then examined the risk factors using multivariable models in Stan. Methods Data on samples collected for monitoring of antimicrobial resistance in Salmonella in swine from 2002 to 2013 and 2015 were derived from the database of Spanish Veterinary Antimicrobial Resistance Surveillance Network programme. Faecal samples were randomly collected annually that altogether added up to more than 50% of the slaughter capacity in Spain each year and that were located in no less than half of the autonomous communities of Spain. Each faecal sample, containing the faeces of two randomly sampled pigs from the same farm, was collected in a sterile container and stored at refrigeration (4°C) until it was sent to the laboratory within the next 24 hours. Salmonella isolation was performed according to ISO 6579:2002/Amd 1:2007, the method recommended by the European Union Reference Laboratory for Salmonella in faecal and environmental samples [6]. To examine potential risk factors for the risk of Salmonella infection at pig farm level in Spain, data related to pig farm practices in Spain were acquired. They included (a) the numbers and density of different type of farms (i.e., the combination of commercial, self-consumption, Intensive and mixed, and extensive farms) in each province (IEP) and (b) the numbers of different stages or types of pigs (i.e., piglets, weaners, fattening pigs, gilts, sows and boars) in different types of farms IEP. The ratios between the numbers of fattening pigs over other pigs IEP were calculated. One sample was randomly selected to establish the farm Salmonella status for farms with more than one sample. Overall, the annual and provincial apparent prevalence was calculated as the number of positive farms over total farms sampled. Empirical Bayesian smoothing was performed to adjust provincial prevalence with Gabriel Graph describing the neighbouring relationships [7]. A Poisson model was fitted with the number of positive farms IEP as the outcome variable and the expected number of positive farms IEP as the offset. Global and local Morans’ I tests were run on the standardised residuals from the model to assess the presence of global and local spatial autocorrelation, respectively [7, 8]. The Poisson model of the spatial scan statistics was employed to detect the presence of areas with increased risk of Salmonella infection at the pig-farm level [9]. The pseudo-P-values for all these statistics were estimated using 999 iterations. Bayesian modelling was performed in RStudio with ‘rstan’ and ‘rstanarm’ packages [10, 11]. Default weakly informative priors of ‘rstanarm’ package were used for the priors. Sampling was done with 4 Markov chains with 1000 iterations. Markov chain Monte Carlo diagnostics, model diagnostics and model selection were facilitated by ‘bayesplot’ and ‘loo’ packages [12, 13]. Poisson models with the same outcome and offset as the aforementioned model examined the potential spatial distribution of the risk of Salmonella infection and its association with pig farm practices in Spain. All covariates were standardised before their inclusion in the model. Two spatial effects, structured and unstructured, were examined in the model. The structured spatial effect, constructed using the CAR model, carried information about the neighbouring relationship between the provinces, and the unstructured consisted of independent province information. Univariable models were fitted for all covariates, at first without considering the spatial effects. Correlation between covariates with a ratio of the mean to the standard deviation (MSD) of the posterior distribution >1.3 (i.e., pseudo-P value ≈ 0.2) was assessed using Spearman test. One of the two highly correlated covariates (i.e., r ≥0.85) along with covariates that had no high correlation with others (r <0.85) were then included in the multivariable model selection. Model selection was performed in two stages. A backward model selection strategy using MSD ratio was first employed, starting with all selected covariates and biologically meaningful pair-wise interactions. The selection process stopped when all the MSD ratios were >1.7. The second stage of model selection was performed by comparing the predictive ability measured by (a) WAIC weights, (b) Pseudo-Bayesian model averaging (PBMA) weights without Bayesian bootstrap, (c) PBMA weights with Bayesian bootstrap, and (d) Bayesian stacking weights of the models. The final model had the highest values from most weighting methods. Models with all variables with MSD ratios >1.96 and with/without those with MSD ratios between 1.7 and 1.96 were considered in this second stage. The effect of adding (a) structured spatial effect, (b) unstructured spatial effect or (c) both were finally assessed in the model with the best predictive ability using the aforementioned weighting methods. Results Up to 3,027 samples over the 14 years were included in the study, with an average of 223 (range: 170-384) samples per year. A median of 60 (interquartile interval: 17.5-90, range: 4-383) samples were collected from 35 different abattoirs over the 13 years. Abattoirs were located in 11 out of 18 autonomous communities in Spain; 804 (26.6%), 652 (21.5%) and 403 (13.3%) samples were from Cataluña, Castilla-La Mancha and Murcia, respectively. The sampled farms were located in 43 out of 52 provinces in Spain; 424 (14.0%) were from Murcia, 261 (8.6%) were from Huesca and Barcelona, respectively, and 252 (8.3%) were from Toledo. Overall, Salmonella prevalence at the farm level was 34.3% (95% confidence interval [CI]: 32.6-36.0). The yearly, provincial and spatially adjusted prevalence are shown in Figure 1-3. Neither global nor local spatial autocorrelation was detected in the residuals of the Poisson model. However, the spatial scan statistics identified local clusters with an increased risk in Salmonella infection at farm level in the northeast and the east of Spain (P<0.03; Figure 4). The structured spatial effect in the univariable model suggested a West-East increasing risk of Salmonella infection in pig farms in Spain (Figure 5). In the final model, only the number of fattening pigs IEP was associated with an increased risk of Salmonella infection, with the risk of infection increased by 1.6% (95% credible interval [CrI]: 0.7−3.2%) each 10,000 increase of fattening pigs. Covariates associated with a decreased risk of Salmonella infection include the number of piglets IEP (1.3% [95% CrI: -0.2−2.7%] per 10,000 piglets), the number of weaners IEP (2.7% [95% CrI: 1.7−3.7%] per 10,000 weaners), and the ratio between the number of fattening pigs and other pigs IEP (15.3% [95% CrI: 4.6−24.9%] per unit). Furthermore, three interactions were associated with a decreased risk: (a) the interaction between the number of piglets and the number of weaners IEP (0.4% [95% CrI: 0.2−0.5] per 10,000 piglets or weaners), (b) the interaction between the number of fattening pigs and the ratio IEP (0.2% [95% CrI: 0.1−0.3%] per 10,000 piglets or per unit of the ratio), and (c) the interaction between the number of piglets and the ratio IEP (0.6% [95% CrI: 0.0−1.2] per 10,000 piglets or per unit of the ratio). None of the spatial effects could be retained in the final model. Conclusion We showed that there were more Salmonella-positive pig farms in eastern and north-eastern Spain than in the rest of the country. We also demonstrated that Stan can serve as an effective and efficient alternative for Bayesian spatial modelling in veterinary epidemiology. The number of fattening pigs IEP is associated with an increased risk of Salmonella infection in pig farms, and the number of piglets, weaners and the ratio between the fattening pigs are associated with a decrease in risk. Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Acknowledgements The authors acknowledge the funding from NOVA (Novel approaches for design and evaluation of cost-effective surveillance across the food chain) project. References 1. European Food Safety Authority (EFSA) and European Centre for Disease Prevention and Control (ECDC). The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2017. EFSA Journal 2018. 2018;16(12):5500. 2. European Food Safety Authority (ESFA) and European Centre for Disease Prevention and Control (ECDC). The European Union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2017. EFSA Journal 2019. 2019;17(2):5598. 3. European Food Safety Authority (EFSA). Report of the Task Force on Zoonoses Data Collection on the Analysis of the baseline survey on the prevalence of Salmonella in slaughter pigs, in the EU, 2006-2007, Part A: Salmonella prevalence estimates. The EFSA Journal 2008. 2008;135:1-111. 4. European Food Safety Authority (EFSA). Analysis of the baseline survey on the prevalence of Salmonella in holdings with breeding pigs, in the EU, 2008, Part A: Salmonella prevalence estimates. EFSA Journal 2009. 2009;7(12):1377. doi: 10.2903.1377. 5. Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. Journal of Statistical Software. 2017;76. doi: 10.18637/jss.v076.i01. 6. International Organization for Standardization. Microbiology of food and animal feeding stuffs - Horizontal method for the detection of Salmonella spp. - Amendment 1: Annex D: Detection of Salmonella spp. in animal faeces and in environmental samples from the primary production stage. Geneva, Switzerland 2007. 7. Roger S Bivand, Edzer Pebesma, Gomez-Rubio V. Applied spatial data analysis with {R}, Second edition: Springer, NY; 2013. 8. Gómez-Rubio V, Ferrándiz-Ferragud J, Lopez-Quílez A. Detecting clusters of disease with R. Journal of Geographical Systems. 2005;7:189-206. 9. Chen C, Kim AY, Ross M, Wakefield J. SpatialEpi: Methods and data for spatial epidemiology. 2018. 10. Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3. 2018. 11. Stan Development Team. RStanArm: Bayesian applied regression modeling via Stan. R package version 2.17.4. 2018. 12. Gabry J, Mahr T, Bürkner P-C, Modrák M, Barrett M. bayesplot: Plotting for Bayesian Models. 1.7.0. 2019. 13. Vehtari A, Gabry J, Yao Y, Gelman A. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2.1.0. 2019. Keywords: spatial analysis, Spatial clustering, CAR model, STAN, Bayesian, Salmonella, Spain, pigs Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019. Presentation Type: Regular oral presentation Topic: Spatio-temporal surveillance and modeling approaches Citation: Teng K, Martinez Aviles M, Ugarte M, Barcena C, De La Torre A, Lopez G and Alvarez J (2019). O, Salmonella, Where Art Thou? Modelling Salmonella infection in swine farms in Spain using Hamiltonian Monte Carlo methods. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00007 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Jun 2019; Published Online: 27 Sep 2019. * Correspondence: Mx. Kendy Tzu-Yun Teng, VISAVET Health Surveillance Centre (UCM), Madrid, Spain, kendy.t.teng@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kendy Tzu-Yun Teng Marta Martinez Aviles Maria Ugarte Carmen Barcena Ana De La Torre Gema Lopez Julio Alvarez Google Kendy Tzu-Yun Teng Marta Martinez Aviles Maria Ugarte Carmen Barcena Ana De La Torre Gema Lopez Julio Alvarez Google Scholar Kendy Tzu-Yun Teng Marta Martinez Aviles Maria Ugarte Carmen Barcena Ana De La Torre Gema Lopez Julio Alvarez PubMed Kendy Tzu-Yun Teng Marta Martinez Aviles Maria Ugarte Carmen Barcena Ana De La Torre Gema Lopez Julio Alvarez Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
In September 2018, classical swine fever (CSF) reemerged in Japan after more than a quarter of a century. After the first notification on a pig farm, wild boars positive for CSF were found continuously in the surrounding area. Gifu was the first prefecture in Japan to disseminate oral vaccines to wild boars in March 2019, with vaccines spread to approximately 14,000 sites between 2019 and 2020. While these diligent measures seemed to have shown some effectiveness, several vaccine spray sites remained without wild boar emergence. Based on the vaccine dissemination records from these periods, this study conducted a statistical analysis to propose more effective vaccine dissemination sites. First, a generalized linear mixed model was used to identify factors correlated with wild boar emergence. Then, two spatial interpolation methods, inverse distance weighted (IDW) and Kriging, were adopted to create a probability map of wild boar emergence for the entire Gifu Prefecture. The analysis showed a positive correlation between wild boar emergence and the appearance of raccoons, raccoon dogs, and crows as well as road density and wild boar distribution index. In particular, raccoon (OR: 1.83, 95%CI: 1.25–2.68, < 0.001), raccoon dog (OR: 1.81, 95%CI: 1.25–2.66, < 0.001), and medium level road density (OR: 1.56, 95%CI: 1.04–2.39, = 0.04) were strongly correlated with wild boar emergence. The spatial interpolation approach resulted in better prediction accuracy for the Kriging method than for IDW by the root mean square error, but both approaches identified a high wild boar appearance probability area in southeastern Gifu and a low appearance probability area in central Gifu. Here we have demonstrated a tool to effectively disperse oral vaccine to wildlife.
Highly pathogenic avian influenza (HPAI) subtype H5N1 continues to circulate across Eurasia and Africa since its unprecedented rapid spread in 2005. Diffusion by wild bird movements has been evidenced in the European Union in 2006 and 2007. Spain is an important wintering quarter for aquatic birds from northern latitudes, so identifying the critical areas and species where an outbreak is prone to happen is necessary. This work presents an assessment of the risk of introduction of H5N1 HPAI in Spain by aquatic wild birds estimating a relative risk value per province. For this purpose, an assessment of the release and exposure to the risk of infection with H5N1 HPAI of 25 selected water bird species has been carried out. Parameters considered in the assessment include H5N1 HPAI notifications from 2006 to 2008 and factors that favour the occurrence or persistence of H5N1 HPAI (wetlands' surface, low temperatures), together with aquatic wild birds' movements parameters (departure, destination, stop-overs, abundance) and parameters relative to the susceptible population in Spain: poultry density and wild aquatic abundance. Results show the relative risk for each Spanish province of experiencing H5N1 HPAI introduced by wild aquatic birds helping to identify higher risk areas.
HPAI virus has caused significant economic losses in the poultry industry. Backyard and outdoor poultry farms (BOPF) can play an important role in the spread of the disease. A spatio-temporal model has been developed to identify areas and periods at higher risk of HPAI spread in BOPF and applied on a Spanish region. Six risk factors were considered: Census, density, biosecurity, species susceptibility, proximity to risk wetlands and virus survival. A risk map was generated adding each risk factor as a spatial layer and a spatial-temporal analysis was conducted using scan statistics. Six clusters of spread risk of HPAI were identified in December and January. Despite the simplicity of the model, this system allows to focus the surveillance efforts in the highest risk areas and species. Thereby it could improve the efficiency of surveillance and control systems in terms of cost/benefit ratio
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