1. Using the pig trade networks and the geographical distance among farms to model the spatio-temporal dynamics of porcine reproductive & respiratory syndrome status at farm level
Center for Animal Disease Modeling and Surveillance (CADMS),
School of Veterinary Medicine, UC Davis
Sara Amirpour Haredasht*, Dale Polson, Rodger Main, Kyuyoung Lee, Derald Holtkamp, Beatriz Martínez-López
*Center for Animal Disease Modeling and Surveillance (CADMS)
Department of Medicine & Epidemiology
School of Veterinary Medicine
University of California, Davis
thanks to the organization for the invitation. It is for me a real pleasure to me to be here today… and being able to talk about the work that we are conducting in Davis

2. Porcine reproductive and respiratory syndrome (PRRS)
Large economic losses in the US (≈$664 million per year)
Little studies quantifying the role that direct (e.g. animal movements) and indirect (e.g. aerosols) contacts had in PRRSV transmission
Objective: To evaluate the ability of animal movement and nearby distance (proxy for airborne transmission) on PRRS status of the farm.

3. Leading Veterinary Medicine, Addressing Societal Needs
MATERIALS AND METHODS
Data
1 system
PRRS status
237 reported PRRS cases
Movement data
3 years
>56,731 movements
>500 production sites
>40 million animals moved
We focused on sow farms, nurseries and GDU and subpopulation of finishers and wean to finisher farms that have reported PRRS.
Leading Veterinary Medicine, Addressing Societal Needs

4. Materials and methods
Diagram of the summary of monthly shipment networks among pig production sites in the US from January 2012 to December Thickness of an arrow and number above arrows explain the mean number of pig shipments in a month during 3 years. The number in parentheses above arrows describes the mean number of pigs transported in a month. The number in boxes explains the mean proportion of production sites in a monthly shipment network among all production sites with a same production type (Lee et al., 2017).

5. Materials and methods
Parameter-driven (PD) spatio- temporal Bayesian approach:
Test the spatio-temporal trend of PRRS reported cases from Jan 2012 to July
To predict the PRRS cases for the last half of based on different weight matrices.
We evaluated the role of geographical distance and/or pig trade in PRRSV status by using five PD models with different weights matrices based on:
geographical distance weight, defined as the inverse distance between each pair of farms in kilometers
pig trade weight, defined as the absolute number of pig movements between each pair of farms
Combination of both (not shown)
R-INLA

6. Geographical distance weight
Results
AUC
DIC
Geographical distance weight
0.83
Pig trade Weight
0.88

7. Results
Posterior mean (µ) for reported PRRS cases of 124 farms for the last half of The farms below the horizontal line (threshold) are predicted negative and the one above the horizontal line are predicted positive. The model prediction accuracy is the sum of the black farms below the threshold and the red farms above the threshold divided by total number of farms (105/124).

8. Results
The number of simulations in which the status of the farm was correctly classified as positive or negative based on the calculated posterior mean from first half of 2014 (5 time steps).

9. Discussion and conclusion
Pig trade plays an important role in PRRSV transmission in this endemic setting.
Our model approach may help to design risk-based surveillance and control strategies for PRRS and other infectious diseases of swine.
Limitations:
During this period there were changes in surveillance associated to the introduction and the massive spread of porcine epidemic diarrhea virus (PEDV) in the US  impact in the accuracy of the model
PD model does not allow formulating the probability of having at least one positive animal in the farm (i.e., probability that a farm is positive) using multiple weight matrices.
Future directions:
The modeling approach of this study may be easily adapted to other production systems to characterize the PRRSV transmission dynamics
This model will be incorporated into Disease BioPortal ( and made available to producers and industry stakeholders.

10. Thank you for your attention!