Identifying questions in the American Association of Swine Veterinarian's PRRS risk assessment survey that are important for retrospectively classifying swine herds according to whether they reported clinical PRRS outbreaks in the previous 3 years

Semi-quantitative risk assessment of African swine fever virus introduction in pig farms

A semi-quantitative risk assessment was developed to classify pig farms in terms of the probability of introduction of African swine fever virus (ASFV). Following on-farm data collection via a specific checklist, we applied a modified failure mode and effect analysis (FMEA) to calculate the risk priority codes (RPC's), indicating increasing risk levels ranging from 1 to 5. The importance of biosecurity measures was attributed by experts. To consider geographic risk factors, we classified pig farms based on local density of farmed pigs, and on the estimated wild boar population density. The combination of RPC's with geographical risk factors resulted into a final ranking of pig farms in terms of the risk of ASFV introduction. Furthermore, the estimation of frequency and levels of non-compliance with biosecurity measures was used to identify weak points in risk prevention at farm level. The outcome of the risk assessment was affected by choices in assigning non-compliance scores and importance to specific components of biosecurity. The method was applied in 60 commercial farms in major pig production areas in Italy. Furthermore, we applied a reduced version of our checklist in 12 non-commercial/small commercial (≤20 pigs) farms in the northern Apennines. In commercial farms, highest RPC's were obtained for biosecurity measures associated with personnel practices and farm buildings/planimetry. Intervention should be addressed to training of personnel on biosecurity and ASF, to avoid contacts with other pig herds, and to improve practices in the entrance into the farm. Sharing trucks with other farms, and loading/unloading of pigs were other weak points. Fencing was classified as insufficient in 70% of the commercial farms. Among these farms, breeding units were characterised by the lowest risk of ASFV introduction (although differences among median ranks were not statistically significant: P-value = 0.07; Kruskal-Wallis test), and increasing herd size was not significantly correlated with a higher risk (Kendall's τ = -0.13; P-value = 0.14). Density of farmed pig was greatest in the main pig production area in northern Italy. Conversely, exposure to wild boars was greatest for non-commercial/small commercial farms on the Apennines, which were also characterised by non-compliance with critical biosecurity measures.

Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants

Background

Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk.

Results

Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction.

Conclusion

The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.

Biosecurity Levels and Farm Characteristics of African Swine Fever Outbreak and Unaffected Farms in Estonia-What Can Be Learned from Them?

Simple Summary

Biosecurity breaches have been shown to play a major role in the introduction and spread of African swine fever (ASF) in domestic pig populations. The aim of the study was to describe the biosecurity levels and management practices of ASF outbreak and uninfected herds and to identify potential risk factors for ASF introduction. The biosecurity score of ASF outbreak herds was significantly lower compared to uninfected herds. This may reflect general improvement in the application of biosecurity measures over time in Estonian pig farms as the data on uninfected herds were collected later, at a time when intensified official controls may have had their effect. Larger herds were more at risk of being ‘outbreak herds’ compared to smaller herds. The biosecurity parameters significantly associated with ‘outbreak herd’ status in statistical analysis were mostly related to indirect contacts with the outside farm environment. The biosecurity barriers applied in Estonian pig farms have not been sufficient to avoid the introduction of ASF and need critical evaluation and improvement. Reduction of all contacts between the farm and the external environment should be emphasized in a situation where ASF is circulating in wild boar populations close to pig farms.

Abstract

Risk factors related to external biosecurity have been considered to play a major role in the introduction and spread of African swine fever (ASF) in domestic pig populations. The aim of the study was to describe the biosecurity levels and management practices of ASF outbreak and uninfected herds and to identify potential risk factors for ASF introduction. Data collected from the outbreak herds during outbreak investigations and from the randomly selected uninfected herds were analyzed. The biosecurity score in ASF outbreak herds was significantly lower compared to uninfected herds. However, this may reflect general improvement in the application of biosecurity measures in pig farms over time as the data on uninfected herds were collected later, at a time when intensified official controls may have had their effect. Larger herds were more at risk of being outbreak herds compared to smaller herds. The biosecurity parameters significantly associated with the outbreak herd status in multiple correspondence analysis were mostly related to indirect contacts with the outside farm environment. The biosecurity barriers applied in Estonian pig farms have not been sufficient to avoid ASF introduction and need critical evaluation and improvement. Reduction of all contacts between the farm and the external environment should be emphasized in a situation where ASF is circulating in wild boar populations close to pig farms.

Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus

Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion; however, quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk has the potential to facilitate better-informed choices of biosecurity practices. Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk and quantify the impact of biosecurity practices on disease risk at individual farms. By quantifying the variable impact on predicted risk, 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to the turnover and number of employees, the surrounding density of swine premises and pigs, the sharing of haul trailers, distance from the public road and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry-relevant diseases.

Construction and efficacy evaluation of novel swine leukocyte antigen (SLA) class I and class II allele-specific poly-T cell epitope vaccines against porcine reproductive and respiratory syndrome virus

Porcine reproductive and respiratory syndrome virus (PRRSV) causes an economically important global swine disease. Here we report the development of subunit PRRSV-2 vaccines by expressing swine leucocyte antigen (SLA) class I and class II allele-specific epitope antigens in a robust adenovirus vector. SLA I-specific CD8 and SLA II-specific CD4 T cell epitopes of PRRSV-2 NADC20 were predicted in silico. Stable murine leukaemia cell lines (RMA-S), which are TAP-deficient and lacking endogenous class I epitope loading, were established to express different SLA I alleles. The binding stability of PRRSV T cell epitope peptides with SLA I alleles expressed on RMA-S cells was characterized. Two PRRSV poly-T cell epitope peptides were designed. NADC20-PP1 included 39 class I epitopes, consisting of 8 top-ranked epitopes specific to each of 5 SLA I alleles, and fused to 5 class II epitopes specific to SLA II alleles. NADC20-PP2, a subset of PP1, included two top-ranked class I epitopes specific to each of the five SLA I alleles. Two vaccine candidates, Ad-NADC20-PP1 and Ad-NADC20-PP2, were constructed by expressing the polytope peptides in a replication-incompetent human adenovirus 5 vector. A vaccination and challenge study in 30 piglets showed that animals vaccinated with the vaccines had numerically lower gross and histopathology lung lesions, and numerically lower PRRSV RNA loads in lung and serum after challenge compared to the controls, although there was no statistical significance. The results suggested that the Ad-NADC20-PP1 and Ad-NADC20-PP2 vaccines provided little or no protection, further highlighting the tremendous challenges faced in developing an effective subunit PRRSV-2 vaccine.

Spatial relative risk and factors associated with porcine reproductive and respiratory syndrome outbreaks in United States breeding herds

Details of incident cases of porcine reproductive and respiratory syndrome (PRRS) in United States breeding herds were obtained from the Morrison's Swine Health Monitoring Project. Herds were classified as cases if they reported an outbreak in a given season of the year and non-cases if they reported it in a season other than the case season or if they did not report a PRRS outbreak in any season. The geographic distribution of cases and non-cases was compared in each season of the year. The density of farms that had a PRRS outbreak during summer was higher in Southern Minnesota and Northwest-central Iowa compared to the density of the underlying population of non-case farms. This does not mean that PRRS outbreaks are more frequent during summer in absolute terms, but that there was a geographical clustering of herds breaking during summer in this area. Similar findings were observed in autumn. In addition, the density of farms reporting spring outbreaks was higher in the Southeast of the United States compared to that of the underlying population of non-case farms. A similar geographical clustering of PRRS outbreaks was observed during winter in the Southeast of the United States. Multivariable analyses, adjusting for the effect of known confounders, showed that the incidence rate of PRRS was significantly lower during winter and autumn during the porcine epidemic diarrhea (PED) epidemic years (2013-2014), compared to PRRS incidence rates observed during the winter and autumn of PED pre-epidemic years (2009-2012). After 2014, an increase in the incidence rate of PRRS was observed during winter and spring but not during autumn or summer. Pig dense areas were associated with a higher incidence rate throughout the year. However, this association tended to be stronger during the summer. Additionally, herds with ≥2500 sows had an increased incidence rate during all seasons except spring compared to those with <2500 sows. PRRS incidence was lower in year-round air-filtered herds compared to non-filtered herds throughout the year. We showed that not only the spatial risk of PRRS varies regionally according to the season of the year, but also that the effect of swine density, herd size and air filtering on PRRS incidence may also vary according to the season of the year. Further studies should investigate regional and seasonal drivers of disease. Breeding herds should maintain high biosecurity standards throughout the year.

Analysis of survey data of breeding herd for reproductive management practices in swine farms of Punjab

The present survey was performed to analyze standard operating procedures for swine development and fertility based on prevailing reproductive management practices among different swine farms of Punjab. The average farrowing rate, farrowing interval, weaning to estrus interval, weaning to conception interval and age at first breeding were 71.5±11.4%, 165.4±13.8 days, 8.3±2.1 days, 42.7±11.0 days and 8.1±1.3 months, respectively. Mean live litter size at birth and weaning were 9.9±3.6 and 8.1±3.3 piglets per farrowing, respectively. Most farmers (94.1%) kept pigs in loose housing system with a mixture of both stalls and pens, and used cement and brick as construction material for sties. Majority of farms (84.3%) functioned as farrow to wean with intensive production systems (75.5%). The labor to animal ratio of 1:50 was most common. Accurate and well maintained records were noticed at 66.7% farms. Start of boar exposure after weaning began within 1 day, occurring most often in morning, with exposure times varying from < 2–5 min/sow in 87.3% farms. Natural mating was allowed within minutes to hours after detection of estrus on 100% of farms. At all farms (100%), sows were allowed 􀁴1 chance for breeding after conception failure before culling. Summer infertility was observed on 56.9% of farms. Feeding method for lactating sows was divided between ad lib. and gradual daily increase of concentrate feed and kitchen waste. None of the farmer practiced docking in newborn piglets. These results suggest that reproductive management of farms in key areas related to weaning, breeding, gestation, feeding and health care could be a source of varying reproductive performance among swine.

Improving the management procedures in farms infected with the Porcine Reproductive and Respiratory Syndrome virus using PDP models

Pig meat production need to be built up in the future due to the increase of the human population worldwide. To address this challenge, there is plenty of room for improvement in terms of pig production efficiency that could be severely hampered by the presence of diseases. In this sense, Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is one of the most costly disease present in industrial pork production in Europe and North America. We have developed a model to analyze the effect of different management procedures to control this important virus in different epidemiological scenarios. Our results clearly suggest that no cross-fostering during lactation and the maintaining of litter integrity significantly decrease the number of sick and dead animals during the rearing period compared to scenarios where cross-fostering and no litter integrity are practiced. These results highlight the relevance of different management strategies to control PRRSV and quantify the effect of limiting cross-fostering and avoiding mixing animals from different litters in PRRSV positive farms to optimize animal production. Our findings will allow pig farmers to apply these management procedures to control this disease under field conditions in a very cost-effective way.

Classification of fetal resilience to porcine reproductive and respiratory syndrome (PRRS) based on temporal viral load in late gestation maternal tissues and fetuses

Although porcine reproductive and respiratory syndrome virus (PRRSV) readily crosses the maternal fetal interface (MFI) in third trimester, fetal resilience varies within litters. The aim of this study was to characterize PRRSV-2 concentration in MFI and fetuses at five time points after experimental inoculation of late gestation gilts and use this information to classify potentially resistant, resilient and susceptible fetuses. The secondary objective was to verify the relationship between PRRS viral load and intrauterine growth retardation (IUGR). Three PRRSV-inoculated pregnant gilts and 1 sham-inoculated control were euthanized at five time points in days post infection (DPI; 2, 5, 8, 12, 14). The preservation status of each fetus was determined and MFI samples adjacent to the umbilical stump of each fetus, as well as serum, thymus, umbilical cord and amniotic fluid were collected. Viral load was quantified using probe-based reverse-transcriptase quantitative PCR (RT-qPCR) targeting PRRSV NVSL 97-7895 ORF7. Our result show the MFI was largely PRRSV infected by 2 DPI and virus was first detected in fetal sera and umbilical cord by 5 DPI, and in fetal thymus and amniotic fluid by 8 DPI. This indicates that PRRSV-2 quickly crossed the placenta and traveled toward the fetus via umbilical circulation within one week of the dam's inoculation. Fetal compromise was first observed on 8 DPI and increased progressively through to 14 DPI. However, several factors were associated with fetal resilience. The random forest model identified that 'viral load in fetal thymus' and duration of infection ('DPI') as the most important factors predicting fetal resilience and resistance. Moreover, IUGR fetuses had lower viral load and were less frequently compromised or dead compared to non-IUGR and average cohorts. Understanding the mechanisms of fetal resilience to PRRSV will improve selection strategies for replacement gilts.

Surveillance of porcine reproductive and respiratory syndrome virus in the United States using risk mapping and species distribution modeling

Porcine reproductive and respiratory syndrome virus (PRRSv) outbreaks cause significant financial losses to the U.S. swine industry, where the pathogen is endemic. Seasonal increases in the number of outbreaks are typically observed using PRRSv epidemic curves. However, the nature and extent to which demographic and environmental factors influence the risk for PRRSv outbreaks in the country remains unclear. The objective of this study was to develop risk maps for PRRSv outbreaks across the United States (U.S.) and compare ecological dynamics of the disease in five of the most important swine production regions of the country. This study integrates spatial information regarding PRRSv surveillance with relevant demographic and environmental factors collected between 2009 and 2016. We used presence-only Maximum Entropy (Maxent), a species distribution modeling approach, to model the spatial risk of PRRSv in swine populations. Data fitted the selected model relatively well when the modeling approach was conducted by region (training and testing AUCs<0.75). All of the Maxent models selected identified high-risk areas, with probabilities greater than 0.5. The relative contribution of pig density to PRRSv risk was highest in pig-densely populated areas (Minnesota, Iowa and North Carolina), whereas climate and land cover were important in areas with relatively low pig densities (Illinois, Indiana, South Dakota, Nebraska, Kansas, Oklahoma, Colorado, and Texas). Although many previous studies associated the risk of PRRSv with high pig density and climatic factors, the study here quantifies, for the first time in the peer-reviewed literature, the spatial variation and relative contribution of these factors across different swine production regions in the U.S. The results will help in the design and implement of early detection, prevention, and control strategies for one of the most devastating diseases affecting the swine industry in the U.S.

Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States

The US swine industry has been impaired over the last 25 years by the far-reaching financial losses caused by the porcine reproductive and respiratory syndrome (PRRS). Here, we explored the relations between the spatial risk of PRRS outbreaks and its phylodynamic history in the U.S during 1998–2016 using ORF5 sequences collected from swine farms in the Midwest region. We used maximum entropy and Bayesian phylodynamic models to generate risk maps for PRRS outbreaks and reconstructed the evolutionary history of three selected phylogenetic clades (A, B and C). High-risk areas for PRRS were best-predicted by pig density and climate seasonality and included Minnesota, Iowa and South Dakota. Phylodynamic models demonstrated that the geographical spread of the three clades followed a heterogeneous spatial diffusion process. Furthermore, PRRS viruses were characterized by typical seasonality in their population size. However, endemic strains were characterized by a substantially slower population growth and evolutionary rates, as well as smaller spatial dispersal rates when compared to emerging strains. We demonstrated the prospects of combining inferences derived from two unique analytical methods to inform decisions related to risk-based interventions of an important pathogen affecting one of the largest food animal industries in the world.

Enhancing heterologous protection in pigs vaccinated with chimeric porcine reproductive and respiratory syndrome virus containing the full-length sequences of shuffled structural genes of multiple heterologous strains

Porcine reproductive and respiratory syndrome virus (PRRSV) is the causative agent of arguably the most economically important global swine disease. The extensive genetic variation of PRRSV strains is a major obstacle for heterologous protection of current vaccines. Previously, we constructed a panel of chimeric viruses containing only the ectodomain sequences of DNA-shuffled structural genes of different PRRSV strains in the backbone of a commercial vaccine, and found that one chimeric virus had an improved cross-protection efficacy. In this present study, to further enhance the cross-protective efficacy against heterologous strains, we constructed a novel chimeric virus VR2385-S3456 containing the full-length sequences of shuffled structural genes (ORFs 3-6) from 6 heterologous PRRSV strains in the backbone of PRRSV strain VR2385. We showed that the chimeric virus VR2385-S3456 induced a high level of neutralizing antibodies in pigs against two heterologous strains. A subsequent vaccination and challenge study in 48 pigs revealed that the chimeric virus VR2385-S3456 conferred an enhanced cross-protection when challenged with heterologous virus strain NADC20 or a contemporary heterologous strain RFLP 1-7-4. The results suggest that the chimera VR2385-S3456 may be a good PRRSV vaccine candidate for further development to confer heterologous protection.

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.