Abstract
BackgroundPorcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PTji) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight.ResultsThe model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124).ConclusionThe result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.
Highlights
Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry
The main pathways of transmission between herds have been described, there are few observational studies characterizing the dynamics of PRRS transmission and quantifying the role and relative importance of each pathway in endemic settings
PRRS surveillance is primarily conducted in sow farms, nurseries and gilt development units (GDUs), as well as in other types of production sites after an outbreak investigation, we focused only on modeling PRRS cases in sow farms, nurseries and GDU, and the subpopulation of finishers and wean-to-finisher farms that had reported PRRS outbreaks in the last 4 years
Summary
Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. A better understanding of transmission patterns under specific endemic settings and the prediction of farms at highest risk of PRRS occurrence in subsequent time periods may help producers to prioritize interventions and minimize farm-to-farm transmission, which will facilitate more cost-effective prevention, control and, elimination of PRRSV at the farm or regional level
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