Assessment of temperature and time on the survivability of porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV) on experimentally contaminated surfaces

Fomites might be responsible for virus introduction in swine farms, highlighting the importance of implementing practices to minimize the probability of virus introduction. The study’s objective was to assess the efficacy of different combinations of temperatures and holding-times on detecting live PRRSV and PEDV on surfaces commonly found in supply entry rooms in swine farms. Two PRRSV isolates (MN 184 and 1-4-4 L1C variant) and one PEDV isolate (NC 49469/2013) were inoculated on cardboard and aluminum. An experimental study tested combinations of four temperatures (20°C, 30°C, 40°C, and 50°C) and six holding-times (15 minutes, 60 minutes, 6 hours, 12 hours, 24 hours, and 36 hours) for the presence of the viruses on each surface type. After virus titration, virus presence was assessed by assessing the cytopathic effects and immunofluorescence staining. The titers were expressed as log10 TCID50/ml, and regression models; half-lives equations were calculated to assess differences between treatments and time to not detect the live virus. The results suggest that the minimum time that surfaces should be held to not detect the virus at 30°C was 24 hours, 40°C required 12 hours, and 50°C required 6 hours; aluminum surfaces took longer to reach the desired temperature compared to cardboard. The results suggest that PRRSV 1-4-4 L1C variant had higher half-lives at higher temperatures than PRRSV MN 184. In conclusion, time and temperature combinations effectively decrease the concentration of PRRSV and PEDV on different surfaces found in supply entry rooms in swine farms.This article is published as Mil-Homens, Mafalda, Ethan Aljets, Rodrigo C. Paiva, Isadora Machado, Guilherme Cezar, Onyekachukwu Osemeke, Daniel Moraes et al. "Assessment of temperature and time on the survivability of porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV) on experimentally contaminated surfaces." Plos one 19, no. 1 (2024): e0291181. doi: https://doi.org/10.1371/journal.pone.0291181. © 2024 Mil-Homens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License

Characterization of changes in productivity parameters as herds transitioned through the modified AASV PRRSV status classification for breeding herds

The American Association of Swine Veterinarians (AASV) system for Porcine reproductive and respiratory syndrome virus (PRRSV) status classification of breeding herds aimed to standardize terminologies; helping harmonize concepts amongst researchers, understanding conditions under which field research was conducted, and helping swine producers/veterinarians understand weaned pig value. This classification system was recently reviewed and modified. This study sought to characterize changes in key productivity parameters as six breed-to-wean herds transitioned between PRRSV statuses as defined by the modified AASV PRRSV classification scheme. Overall, it was possible to assign the modified AASV PRRSV statuses to farm-weeks. There was a statistically significant improvement in measured productivity parameters as breeding herds improved PRRSV status

Practical approaches to improving PRRSV surveillance in weaning-age pig populations in the United States

The threat posed by the porcine reproductive and respiratory syndrome virus (PRRSV) to global swine production is evident and irrefutable. The ecology of the virus poses a challenge to clearly understanding its activity in infected swine populations; consequently, it is not uncommon for swine practitioners to have an unsettling uncertainty about the status of their herds even after instituted control and elimination programs have been appraised. Notwithstanding, an appraisal of instituted programs by efficient monitoring and surveillance systems (MOSS) remains pivotal in understanding the PRRSV status of herds. The compartmentalized structure of conventional swine barns (having securely closed crates/pens sharing common airspace within rooms, well-barricaded rooms within barns, and independent barns within sites), the ability of PRRSV to sustain low prevalence, and the reported clustering of PRRSV across space and subpopulations, are some of the reasons why sampling for MOSS needs to be strategic. The weaning-age pig subpopulation is the most suitable for ascertaining the PRRSV shedding status of breeding herds; however, few sampling options have been characterized for surveilling this subpopulation. The clear characterization of sample options specifically for this subpopulation, the provision of easy-to-implement sampling guidelines, and the exploration of cost-efficient surveillance strategies will not only incentivize swine practitioners with the needed tools to explore the weaning-age pig subpopulation but will also help in forestalling the unpleasant eventualities associated with false inferences made from ineffective MOSS. For the above reasons, this dissertation aims to improve MOSS in weaning-age piglets by addressing the highlighted issues. Within Chapter One of this dissertation, a literature review highlighted knowledge gaps in PRRSV MOSS aimed at finding active PRRSV infections in suckling pig populations using reverse transcription polymerase chain reaction (RT-PCR) tests. Of the 1,270 records obtained from two databases, 25 full-text peer-reviewed articles met inclusion criteria and were reviewed. Summarily, there was sparse information on the use- and performance of non-aggregate non-serum antemortem sample types for PRRSV MOSS in suckling pigs; there was neither a basis for ascertaining sample sizes nor context for interpreting RT-PCR test results using non-serum antemortem sample types. Furthermore, the cost-saving strategy of pooling antemortem sample types had been largely unexplored. Sample size considerations for aggregate samples had also not been delineated. In Chapter Two, the study's objective was to compare PRRSV RNA detection rates in oral swabs to detection rates in serum samples obtained from naturally infected weaning-age litters under field conditions. The relationship between the proportion of piglets testing positive for either sample type was also assessed. Out of 623 piglets sampled from 51 litters, 83 piglets across 24 litters were RT-PCR positive on tested serum, and 33 piglets across 15 litters were RT-PCR positive on tested oral swabs. The RT-PCR cycle threshold (Ct) values were lower in serum samples (18.9 to 32.0) than in oral swab samples (28.2 to 36.9). To be ≥ 95% confident of ≥ 1 positive OS test from a fully sampled litter, ≥ 35% of piglets within that litter had to be viremic. All the litters (n = 27) with no viremic piglets also had negative OS tests. Chapter Three explored PRRSV RNA detection rates in more sample types from weaning-age litters with the end goal of estimating the diagnostic performance of the sample types and the probability of PRRSV RNA detection in litter-level pools. Serum, ear-vein blood swabs (ES), nasal swabs (NS), oral swabs (OS), and family oral fluids (FOF) were sampled from 55 litters having 666 piglets in total. Across all three sampled herds, 96 piglets distributed across 22 litters were PRRSV RT-PCR positive on serum, 80 piglets across 15 litters were positive on ES, 80 piglets across 17 litters were positive on OS, and 72 piglets across 14 litters were positive on NS. Cohen’s kappa analyses showed near-perfect agreement between all paired ES, OS, NS, and serum comparisons (C_k\geq0.81). The serum RT-PCR cycle threshold values were the lowest generally and strongly predicted PRRSV detection in swab samples. There was a ≥ 95% probability of PRRSV detection in ES-, OS-, and NS pools when the proportion of positive swab samples was ≥ 23%, ≥ 27%, and ≥ 26%, respectively. Under the conditions of the study, when PRRSV was at ≥ 10% prevalence, the estimated number of sera, ES, NS, and OS to be sampled to be ≥ 95% confident of detecting ≥ 1 positive piglet was 30, 36, 36, and 40, respectively. Chapter Four demonstrated the effect of pooling PRRSV RNA-positive FOF samples with different proportions (1/3, 1/5, 1/10, 1/20) of PRRSV RNA-negative FOF samples. A laboratory experiment was done using PRRSV-positive FOF samples obtained from PRRSV-positive herds, and PRRSV-negative samples obtained from PRRSV-naïve herds. Mathematical models were used to evaluate changes in the probability of PRRSV RT-PCR detection with increasing proportion of PRRSV-positive samples within pools. The effect of partially sampling a farrowing room on the probability of PRRSV RT-PCR detection in pooled samples was also assessed. At the pooled sample level, the probability of PRRSV RT-PCR detection decreased from 100 % to 87 %, 68 %, and 26 % when diluting up to 1/20 for a PRRSV RNA-positive FOF sample having an initial Ct value below 34, between 34 and 36, or above 36, respectively. A FOF sampling and pooling guideline was presented in this study; as an example, when 1 or 2 litters are PRRSV-positive in a 56-crate room, the most cost-efficient sampling strategy to detect PRRSV RNA by RT-PCR with at least 95 % certainty was pooling FOF samples up to 1/10 and testing ≥ 5 such pools. In Chapter Five, mathematical models were used to characterize the relationship between the proportion of viremic piglets, the proportion of litters with at least one viremic piglet-, and the expected RT-PCR positivity rate of FOF samples obtained from a farrowing room. A stochastic model was built using parameters calculated from two published studies. In this chapter, the spatial clustering of viremic piglets within a farrowing room was quantifiable and reproducible. A clustering value of 0 would imply a completely random distribution of viremic piglets in a farrowing room, while a clustering value of 1 would restrict all viremic piglets within the fewest litters possible; the median clustering measured from one of the reference studies was 0.61. There was a linear relationship between the proportion of positive piglets- and the proportion of positive litters in a farrowing room. At a clustering value of 0.61 in a 56-crate room, when the proportion of viremic piglets was 1.00%, 5.00%, 10.00%, 20.00%, and 50%, the proportion of litters with ≥ 1 viremic piglets was 5.36%, 8.93%, 14.29%, 23.21%, and 53.57% respectively. The corresponding expected positivity rate in sampled FOF was 2.06%, 6.48%, 11.25%, 21.60%, and 51.56%, respectively. Tables with similarly matched proportions at different spatial clustering settings and room sizes are provided in this study to help guide sampling considerations and provide information on the most probable piglet-level PRRSV prevalence given the results of RT-PCR on FOF

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, 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 risk have the potential to facilitate better informed choices. 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, 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. 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 trailers; distance from the public road; and production type. In addition, the development of individualized biosecurity assessments provides the opportunity to guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it potential to be applied to wider areas of biosecurity benchmarking, to address weaknesses in other livestock systems and industry relevant diseases

Characterization of changes in productivity parameters as breeding herds transitioned through the 2021 PRRSV Breeding Herd Classification System

Using retrospective data from 6 breed-to-wean herds over 4 years, porcine reproductive and respiratory syndrome virus (PRRSV) statuses were assigned by week according to the 2021 American Association of Swine Veterinarians PRRSV classification. Productivity changes were characterized as herds transitioned through status categories. Overall, productivity improved as farm status improved.This article is published as Osemeke, Onyekachukwu H., Tara Donovan, Kate Dion, Derald J. Holtkamp, and Daniel CL Linhares. "Characterization of changes in productivity parameters as breeding herds transitioned through the 2021 PRRSV Breeding Herd Classification System." Journal of Swine Health and Production 30, no. 3 (2022): 145-148. DOI: 10.54846/jshap/1269. Copyright 2022 AASV. Posted with permission

Evaluating oral swab samples for PRRSV surveillance in weaning-age pigs under field conditions

Introduction: The use of serum and family oral fluids for porcine reproductive and respiratory syndrome virus (PRRSV) surveillance in weaning-age pigs has been previously characterized. Characterizing more sample types similarly offers veterinarians and producers additional validated sample options for PRRSV surveillance in this subpopulation of pigs. Oral swab sampling is relatively easy and convenient; however, there is sparse information on how it compares to the reference sample type for PRRSV surveillance under field conditions. Therefore, this study's objective was to compare the PRRSV reverse-transcription real-time polymerase chain reaction (RT-rtPCR) test outcomes of oral swabs (OS) and sera samples obtained from weaning-age pig litters. Method: At an eligible breeding herd, six hundred twenty-three weaning-age piglets from 51 litters were each sampled for serum and OS and tested for PRRSV RNA by RT-rtPCR. Results and Discussion: PRRSV RT-rtPCR positivity rate was higher in serum samples (24 of 51 litters, 83 of 623 pigs, with a mean cycle threshold (Ct) value of RT-rtPCR-positive samples per litter ranging from 18.9 to 32.0) compared to OS samples (15 of 51 litters, 33 of 623 pigs, with a mean Ct of RT-rtPCR positive samples per litter ranging from 28.2 to 36.9); this highlights the importance of interpreting negative RT-rtPCR results from OS samples with caution. Every litter with a positive PRRSV RT-rtPCR OS had at least one viremic piglet, highlighting the authenticity of positive PRRSV RT-rtPCR tests using OS; in other words, there was no evidence of environmental PRRSV RNA being detected in OS. Cohen's kappa analysis (Ck = 0.638) indicated a substantial agreement between both sample types for identifying the true PRRSV status of weaning-age pigs.This article is published as Osemeke, Onyekachukwu Henry, Nathan VanKley, Claire LeFevre, Christina Peterson, and Daniel CL Linhares. "Evaluating oral swab samples for PRRSV surveillance in weaning-age pigs under field conditions." Frontiers in Veterinary Science 10 (2023): 73. DOI: 10.3389/fvets.2023.1072682. Copyright 2023 Osemeke, VanKley, LeFevre, Peterson and Linhares. Attribution 4.0 International (CC BY 4.0). Posted with permission

In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room

Background: Family oral fluids (FOF) sampling has been described as a sampling technique where a rope is exposed to sows and respective suckling litters and thereafter wrung to obtain fluids. PCR-based testing of FOF reveals presence of PRRS virus RNA only at the litter level, as opposed to conventional individual-animal-based sampling methods that demonstrate PRRSV RNA at the piglet level. The relationship between the PRRSV prevalence at the individual piglet level and at the litter level in a farrowing room has not been previously characterized. Using Monte Carlo simulations and data from a previous study, the relationship between the proportion of PRRSV-positive (viremic) pigs in the farrowing room, the proportion of litters in the farrowing room with at least one viremic pig, and the likely proportion of litters to be positive by a FOF RT-rtPCR test in a farrowing room was characterized, taking into account the spatial distribution (homogeneity) of viremic pigs within farrowing rooms. Results: There was a linear relationship between piglet-level- and litter-level prevalence, where the latter was always larger than the former. When the piglet-level prevalence was 1%, 5%, 10%, 20%, and 50%, the true-litter level prevalence was 5.36%, 8.93%, 14.29%, 23.21%, and 53.57%, respectively. The corresponding apparent-litter prevalence by FOF was 2.06%, 6.48%, 11.25%, 21.60%, and 51.56%, respectively. Conclusion: This study provides matching prevalence estimates to help guide sample size calculations. It also provides a framework to estimate the likely proportion of viremic pigs, given the PRRSV RT-rtPCR positivity rate of FOF samples submitted from a farrowing room.This article is published as Osemeke, Onyekachukwu H., Eduardo de Freitas Costa, Vinicius Weide, Swaminathan Jayaraman, Gustavo S. Silva, and Daniel CL Linhares. "In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room." Porcine Health Management 9, no. 1 (2023): 1-9. DOI: 10.1186/s40813-023-00309-x. Copyright 2023 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission

In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room

Background: Family oral fluids (FOF) sampling has been described as a sampling technique where a rope is exposed to sows and respective suckling litters and thereafter wrung to obtain fluids. PCR-based testing of FOF reveals presence of PRRS virus RNA only at the litter level, as opposed to conventional individual-animal-based sampling methods that demonstrate PRRSV RNA at the piglet level. The relationship between the PRRSV prevalence at the individual piglet level and at the litter level in a farrowing room has not been previously characterized. Using Monte Carlo simulations and data from a previous study, the relationship between the proportion of PRRSV-positive (viremic) pigs in the farrowing room, the proportion of litters in the farrowing room with at least one viremic pig, and the likely proportion of litters to be positive by a FOF RT-rtPCR test in a farrowing room was characterized, taking into account the spatial distribution (homogeneity) of viremic pigs within farrowing rooms. Results: There was a linear relationship between piglet-level- and litter-level prevalence, where the latter was always larger than the former. When the piglet-level prevalence was 1%, 5%, 10%, 20%, and 50%, the true-litter level prevalence was 5.36%, 8.93%, 14.29%, 23.21%, and 53.57%, respectively. The corresponding apparent-litter prevalence by FOF was 2.06%, 6.48%, 11.25%, 21.60%, and 51.56%, respectively. Conclusion: This study provides matching prevalence estimates to help guide sample size calculations. It also provides a framework to estimate the likely proportion of viremic pigs, given the PRRSV RT-rtPCR positivity rate of FOF samples submitted from a farrowing room

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, 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 risk have the potential to facilitate better informed choices. 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, 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. 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 trailers; distance from the public road; and production type. In addition, the development of individualized biosecurity assessments provides the opportunity to guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it potential to be applied to wider areas of biosecurity benchmarking, to address weaknesses in other livestock systems and industry relevant diseases.This is a pre-print of the article Sykes, Abagael L., Gustavo S. Silva, Derald J. Holtkamp, Broc W. Mauch, Onyekachukwu Osemeke, Daniel CL Linhares, and Gustavo Machado. "Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus." arXiv preprint arXiv:2106.06506 (2021). Posted with permission.</p