A descriptive study of on-farm biosecurity and management practices during the incursion of porcine epidemic diarrhea into Canadian swine herds, 2014

In May 2013, the first swine herd in the United States, located in Iowa, was diagnosed with porcine epidemic diarrhea virus (PEDV). Thirty of 50 states in the United States were affected by the virus within one year of PEDV emergence [1]. The first PEDV strain found in the United States was found to phylogenetically cluster within a subgroup containing a strain that was discovered in China in 2012 [2]. A second PEDV strain was also detected by Iowa State University and the Ohio Department of Agriculture [3]. This strain however clustered with a different Chinese strain reported from 2010 to 2012. This suggests that there may have been: 1) numerous ways the virus was introduced, or 2) a single introduction of multiple PEDV strains [4]. Transmission of PEDV between pigs on a farm occurs by direct and indirect fecal-oral routes [5]. Viral transmission between farms has been linked to animal movements containing PED-positive animals, feces contaminated fomites including trucks, barn boots, and equipment, along with the movement of people on and off farm [6, 7, 8, 9].

In January 2014, the first Canadian swine herd, located in Ontario, was diagnosed with PEDV. This case occurred approximately nine months following the initial case of PED in the United States [4]. Porcine epidemic diarrhea (PED) has a significant impact on animal health and profitability of individual farms [10], as the virus is highly contagious and is associated with mortality ranging between 80 and 100% in suckling pigs [8]. Unlike the dynamics of the virus in the United States, the emergence of the virus into Canada was thought to be from a common source. Spray-dried porcine plasma (SDPP), a feed ingredient added to nursery feed for improving growth and overall health [11] was thought to be the source of the infection. During the initial phase of the outbreak, a descriptive outbreak investigation and an experimental challenge study were undertaken to determine the likely source of infection. In a previous research study, a case-control study was designed, which utilized data from an extensive questionnaire to analyze a subset of the collected variables using logistic regression [12] and network analysis [13]. In both cases, the common source hypothesis (through feed) was confirmed [12, 13]. In addition, in the case-control study, a biosecurity score was created based on the adoption of four beneficial biosecurity practices: 1) washing and disinfecting livestock trailers prior to delivering pigs onto the site, 2) truck drivers not stepping out of the vehicle and entering the laneway on-site, 3) enforcement of a shower-in policy or bench entry was implemented on-site, and 4) requirement of staff and non-staff members to wear site specific clothing [12]. It was found that the biosecurity score was a risk factor for PED during univariable analysis, which was in agreement with previous reports claiming that PED-affected herds were the herds with a high level of biosecurity [14]. Nonetheless, the questionnaire administered as part of the case-control study contained an abundance of information regarding biosecurity and farm management practices that were in place at the time of the 2014 PED outbreak. Therefore, the primary objective of this study was to describe the general biosecurity and management practices that were in place in sow herds that contracted PEDV during the early phase of the 2014 outbreak, and to compare these herds to matched control herds. The secondary objective was to determine whether, after exclusion of the variables reflective of common source of infection, there were any general biosecurity and farm management practices that were important in explaining PEDV status. In order to address the latter objective, we used a non-parametric approach based on random forests (RFs) [15], which allowed assessment of variable importance and predictive accuracy based on cross-validation. The source of contaminated feed was excluded from the analysis of the current study, because the focus of this study was on general biosecurity practices potentially associated with the outbreaks.

The study protocol and animal use were reviewed and approved by the University of Guelph's Animal Care (AUP# 3094) and Research Ethics Board (REB #14AP030) committees.

Prior to January 2014, Canadian swine industry members, stakeholders and veterinarians were preparing for the emergence of PEDV, due to the rapid spread of the virus observed months prior in the United States and the high connectivity between the Canadian and United States industries. The virus has significantly affected the swine industry due to production losses as well as financial commitments for intervention and surveillance programs [21]. The production losses are due to reduced performance in breeding herds as a result of high morbidity and mortality that occurs in immunologically naïve neonatal piglets. Poor performance is also evident in finishing herds due to periods of increased clinical illness (i.e. diarrhea and vomiting). Given that the routes of transmission for PEDV have been established within and between herds, including: animal movements, truck movements, people, equipment, feed and herd biosecurity [6, 7, 8, 9], these were the areas of focus for the current study.

There are many truck movements that transport nursery pigs, market hogs, and cull sows from the province of Ontario (Canada) to the United States. In 2014, 3.9 million feeder pigs were exported from Canada to the United States, which accounted for 79.5% of the United States total imports [22]. It has been previously reported in the United States that transport trucks are typically shared between producers and sites and are often not disinfected or washed in between loads, which can enable the spread of PEDV [23]. After the trucks are emptied in the United States, they often return to Ontario without being washed, disinfected and dried [23]. Additionally, in a study conducted by Lowe et al. [24], it was found that animal movement and biosecurity practices related to animal transportation (trucks and trailers) played a role in viral transmission. Lowe et al. [24] reported that 4.9% PEDV-free trucks that delivered pigs to abattoirs were cross-contaminated after the pigs were unloaded. For this reason, it was initially speculated that the emergence of PED into Canada would be due to contaminated trucks from the United States. Although, the biosecurity practices based on transportation, truck drivers and herd management practices descriptively favour case herds over control herds in the current study. For instance, 38% of case herds received pig deliveries from transport trailers that were washed and disinfected prior to delivering pigs on-site compared to 33% of control herds. Thirty-eight percent of case herds reported that truck drivers helped unload pigs, stepped on the laneway and entered the barn when delivering pigs on-site compared to 17% of control herds. When pigs were delivered off-site to either a different farm, abattoir, deadstock or culled animal site, 63% of case herds reported that the pigs were delivered in transport trailers that were washed and disinfected compared to 42% of control herds. Also, 88% of staff and non-staff members were required to shower on-site prior to entering the barn compared to 58% of control herds. Case herds also had fewer numbers of deadstock movements picked up by contracted deadstock companies compared to control herds. Thus, case herds seemed to have better biosecurity practices related to herd management and animal movements including deadstock and culled animals with regards to transportation and truck driver protocols. These findings were also determined analytically by Perri et al. [12] and descriptively by Pasma et al. [14]. This indicates that biosecurity related to animal movements, transportation vehicles and deadstock removal likely did not play a role in the incursion of PEDV into Canada.

With regards to feed management practices, feed suppliers and potentially contaminated SDPP were not included in the current study. Viral infection based on feed is likely a multi-factorial process. For instance, specific feed ingredients capable of harbouring viable PEDV over a long period of time becomes contaminated, followed by the contaminated ingredients contacting a supportive matrix during the processing of complete feed [10]. Feed deliveries consisting of the PEDV-positive feed transmit the virus to pigs on farm. Dee et al. [10] found that PEDV survival is specific to certain ingredients. Dee et al. [10] also found that PEDV survived longer in soybean meal which may have been due to environmental temperatures since decreased titers were observed with increased temperature over time. This is because coronaviruses including PEDV can typically survive temperatures from 56°C for 10–15 min, 37°C for several days, 4°C for numerous months, and while frozen at −60°C for many years without losing infectivity [25]. In the current study, the case herds received a higher amount of feed deliveries on-site that consisted of pelleted and bagged feed compared to control herds. Control herds received more feed ingredient deliveries such as soybean meal. Regarding feed storage, case herds stored more feed inside the barn in warm areas than control herds. Whereas, control herds stored more of their feed ingredients indoors and outdoors in cold areas (i.e. in non-insulated areas) located in storage bins. The study period of interest occurred during the winter months in Canada and thus, feed storage (i.e. if stored outside) could have increased the risk of the viability of the virus if the complete feed or feed ingredients were contaminated. Although, if temperature (i.e. colder temperature) were to be associated with PEDV herd status during the incursion of the virus, this would be counterintuitive for the current study since the control herds stored their feed in colder temperatures. To further understand whether feed management practices influenced a herd's PEDV status, RF models were applied.

The machine learning tool RF was used to provide a unique perspective on determining the most important variables for predicting PEDV. The most important variables found to predict herd status (case versus control herds) included herd size and variables related to feed management. All 7 importance measures used to determine variable importance consistently found that following HPU, the total quantity of feed (tonnes) delivered as well as the number of feed deliveries received from off-site feed suppliers were the next top variables for explaining herd status. Although variables related to contaminated plasma were excluded from the model, the variables related to feed were still identified as the most important ones. It is possible that this is due to partial confounding between different feed-related variables. The final model based on a limited set of variables had a moderate predictive accuracy (72%) using cross-validation, although the sensitivity of this model was still poor and indicated that only 45% of case herds were expected to be correctly identified. This predictive accuracy suggests that it was expected that the PED status of 72% of study herds during this study period, (which were not used to train the data) could be accurately predicted using this model. As such, the model has modest accuracy and results should be interpreted with care. Modest accuracy was likely a consequence of withholding an important predictor (exposure to contaminated porcine plasma) from the model. The predictive accuracy further suggests that relying on biosecurity practices that were important predictors of PED status was not a strategy that resulted in great model performance, and we believe that this was likely due to positive correlation between feed-related variables and exposure to contaminated porcine plasma. An indication of this was the high importance of herd size and feed-related variables, among all the biosecurity variables considered in the model. External validity using herds from different study periods was not considered in this study. We expect that this model cannot be extrapolated to other study periods because of all feed-biosecurity measures that were implemented shortly after PED incursion.

The current study does present some limitations. Of the 27 identified case herds during the study period, only 8 sow herds participated in the study. This moderate response rate (30%) is likely due to producers not having enough time to participate especially in the immediate post-outbreak phase, as well as the sensitive nature of the early phase of the outbreak. It was found that the case herds that participated in the study were larger in production size based on HPU compared to control herds, despite efforts to match on the herd size category. This finding likely had an effect on why case herds had more pig movements, higher quantities of pigs delivered, feed deliveries, quantity of feed, non-staff movements on-site and semen deliveries. The next limitation of the study is the sample size. There were 20 herds included in the study with > 100 predictive variables evaluated. However, the RF tree-based machine learning tool splits nodes one step-at-a-time which permits trees (and thus forests) to implement regularization in data sets with large amounts of predictors and a small sample size [15]. Also, the grouping property of trees allows RF to competently account for correlated variables [15]. An advantage of using RF permutation accuracy as an importance measure rather than univariate screening models is that it accounts for each predictor variable individually as well as in multivariate interactions with all other predictor variables [15]. Using RF can increase the prediction accuracy compared to single classification trees because the ensemble method adjusts and accounts for instability of individual trees by small changes in the learning sample, which impairs the prediction accuracy in test samples [26]. Another limitation of using RF is that the interpretation of the model is not as straightforward compared to an individual classification tree. However, this study incorporated 7 measures of importance to determine the top variables for predicting the model. The importance measures were all highly correlated, however the results were consistent when comparing the top 10 importance variables.

In conclusion, the study provided a detailed descriptive analysis of general biosecurity and management practices implemented in PEDV-positive sow herds during the early phase of the 2014 outbreak and in matched control herds. The study focussed solely on the incursion of PEDV into Canadian swine herds during the period of January 22 and March 1, 2014. Detailed descriptions of biosecurity and feed management on case and matched control herds have added insight to the on-farm practices because most of the data obtained and recorded is not readily found in the literature and fills this knowledge gap. A RF machine learning tool determined that along with herd size, 70% of the importance variables for predicting herd status (case versus control herds) were feed management protocols. The final RF model provided a moderate predictive accuracy (72%) and poor sensitivity (45%). The poor sensitivity of this model is problematic due to the nature of the virus. Since the common source of infection was not included, the RF model determined herd size and feed management variables were of importance for predicting herd status, which is likely due to partial confounding. Overall, this study has provided novel insight into the role of biosecurity and feed management practices at the time PEDV emerged into Canadian swine herds in 2014.

Supplementary Table 1

Variable names and definitions used in the study to predict the herd status during the incursion of porcine epidemic diarrhea virus in Canadian swine herds using random forests

Supplementary Table 2

Water source and treatment used for PED-affected and non-PED-affected^* swine herds

Supplementary Table 3

The frequency of deliveries by feed ingredients received by PED-affected and non-PED-affected^* Canadian swine herds

Supplementary Table 4

A summary of the variable importance measures^* for the top variables for predicting porcine epidemic diarrhea virus during the incursion of the virus in Canadian swine herds

Supplementary Fig. 1

A depiction to explain the concept of minimal depth in random forests. The top black node is referred to as the root node. The numbers represent the minimal depth of each node. The variables that split closest to the root node have a strong effect on the predictive accuracy of the random forest model and a higher importance measure.

Supplementary Fig. 2

Relationship between measures of importance^* for predicting porcine epidemic diarrhea virus during the incursion of the virus in Canadian swine herds.

Supplementary Fig. 3

Variable importance using mean decrease in accuracy for predicting porcine epidemic diarrhea virus by type of management practice during the incursion of the virus in Canadian swine herds, 2014.

Supplementary Fig. 4

Variable importance using Gini decrease for predicting porcine epidemic diarrhea virus by type of management practice during the incursion of the virus in Canadian swine herds, 2014.

Supplementary Fig. 5

Partial dependence plots for the top three importance variables for predicting PEDV during the incursion of the virus in Canadian swine herds, 2014.