US PRRSv surveillance using risk mapping and species distribution modeling

Today, we are sharing a publication from the Preventive Veterinary Medicine journal, by Dr. Andres Perez and the STEMMA laboratory. The goal of the study was to quantify the combined effect of factors such as season and herd size on the spatial range of high-risk areas for PRRSV outbreaks. Using Species Distribution Model, the team extracted associations between hypothesized risk factors and disease occurrence.

Highlights

  • A species distribution model was used, to predict the spatial risk of PRRSv in swine populations across the U.S.
  • All of the Maxent spatial models identified high-risk areas, with probabilities greater than 0.5.
  • Relative contribution of pig density to PRRSv risk was higher in densely pig populated areas.
  • Relative contribution of climate and land cover to PRRSv risk were important in areas with relative low pig densities.
  • Ecological dynamics of PRRSv are different between swine production region in the U.S.

The largest number of PRRSv outbreaks in the U.S., as reported in the MSHMP, was observed in north central parts of Iowa, followed by south central areas of Minnesota. However, our crude U.S. Maxent model identified eastern North Carolina, southern Minnesota, and northern Iowa as high-risk areas for PRRSv outbreaks. As expected, pig density accounted for most of the PRRSv spatial risk (81.3% relative contribution). Climate (interpreted as the percentage of day-to-night temperature oscillation compared with the summer-to-winter oscillation, and mean temperature of the warmest quarter) accounted for the remaining spatial risk. Overall, the crude Maxent model suggested geographical areas with high pig densities and with a low level of daily temperature variability to the year are mostly suitable for circulation and maintenance of PRRSv.

Factors percent contribution PRRS outbreak Perez 2017
Summary charts of the estimated relative percent contribution of each environmental and demographic variable of the final Maxent model for each swine production region in the U.S.

The model for the South East region indicated that pig density was the most important predictor; followed by precipitation of the wettest month, land cover, and temperature seasonality. The relative contribution of pig density was smaller for this region compared to the Midwest. Specifically, geographical locations with high pig density, precipitation amount between 120 and 200 mm during the wettest months, and that were located within croplands were mostly suitable for PRRSv outbreaks in North Carolina and Northern South Carolina.

Additionally, the spread of PRRSV under certain conditions was more evident for the regions where pig density is relatively low. For example, in Illinois and Indiana and Kansas, Colorado, Oklahoma and Texas, wet weather and temperatures above 0 °C were more important in predicting the spatial risk of PRRSv than pig density.

Click here to read the entire publication on US PRRSv surveillance using risk mapping and species distribution modeling.

 

PRedicting and mapping PRRSV outbreaks PEREZ 2017.jpg

Abstract

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.

Using Machine Learning to Predict Swine Movements

A collaborative work between the University of Minnesota, UC- Davis, and Pipestone Veterinary Services was published this past month in the journal Frontiers in Veterinary Science.
Between-farm animal movement, despite being an essential factor of infectious disease spread is not currently recorded in the US. The objective of this project was to create a model to predict animal movement based on between-site distance, ownership, and production type of the sending and receiving farms. The model was able to predict animal movement in the south-central region of the study area with a high aggregation. It also showed an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome (PRRS) in this area.

valdes-donoso-machine-learning-pig-movement

Abstract: Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available.

Link to the full article