PRRS Epidemiology: Best Principles of Control at a Regional Level - Dr. Andres Perez, University of Minnesota, from the 2015 North American PRRS Symposium, December 4 - 5, 2015, Chicago, IL, USA. More presentations at http://www.swinecast.com/2015-north-american-prrs-symposium

1. PRRS Epidemiology
Best principles of control at a regional level
Andres Perez, DVM, PhD
Endowed Chair of Global Animal Health and Food Safety
University of Minnesota
aperez@umn.edu
Chicago, December 2015

2. Swine Group, University of Minnesota
Mike Murtaugh: Xiong Wang
Bob Morrison: Carl Betlach
Andres Perez: Pablo Valdes, Moh Alkhamis, Julio Alvarez, Kim VanderWaal
Team work
Project participants
Swine Health Monitoring Program: 4 anonymous participants
Regional Control Program N212 (RCP N212): Dave Wright (coordinator) and
anonymous program participants
Sponsors
Swine Health Information Center
National Pork Board
University of Minnesota Population Systems and MnDrive programs
Boehringer Ingelheim

3. A. Vaccination or exposure to live-virus
B. Elimination +sows (test-removal)
C. On-site/off-site testng
D. Cleaning & disinfections of trucks
E. Aerosol filtration
F. Improve biosecurity
Control at the farm level
“Strategies to control at the farm-level perform reasonably well, but we still
need to understand how to control the disease at the regional level”
(Polson, Mondaca, Cano 2006)

4. To develop methodological frameworks to:
1. Evaluate progress of Regional Control Programs, RCPs
(study 1)
2. Identify the emergence and monitor the spread of new
PRRSv strains (study 2)
Objectives

5. Study 1: Methodological framework to
evaluate progress of RCPs
Pablo Valdes-Donoso, Lovell S. Jarvis, Dave Wright, Julio Alvarez, Andres M. Perez.
Measuring progress on the control of porcine reproductive and respiratory syndrome
(PRRS) at a regional level: the Minnesota N212 regional control project (RCP) as a
working example. PLOS One. Submitted.
Objectives: To evaluate:
1. Demographics of enrollment
2. Demographics of active participation (sharing PRRS
status)
3. Trend and spatial distribution of incident cases

6. Data: N212
6
A. Geographical location
B. Date of enrollment
C. PRRS status (weekly)
D. Type of farms
1. Farm level
2. Longitudinal (weekly)
3. June 2012 – June 2014 (2 years)

7. 1. Demographics of enrollment
- Proportion of farms (with sows, SS and without sows, NSS)
enrolled in the RCP-N212
- ANOVA
- Null hypothesis: the proportion of SS and NS farms enrolled
in the RCP-N212 was constant through time
Statistical analysis (1/3)

8. 2. Demographics of active participation
- GLME model for binary response
- Response variable: Sharing PRRS status (Y=1, N=0)
- Effects:
- Fixed Effects: Time, farm type
- Random Effects: Site, county
- Spatial and temporal analysis of sharing PRRS:
- Normal Scan Statistic Test (SaTScan)
Statistical analysis (2/3)

9. 3. Trend and distribution of PRRS incidence
Statistical analysis (3/3)
-GLME model for binary response
- Response variable: PRRS status (Pos=1, Neg=0)
- Effects:
- Fixed Effects: Time, farm type, probability of reporting PRRS, Farm
density in the county, Proportion of vaccinated farms in the county
- Random Effects: Site, county
- Time-space correlation of pairs of incident cases

10. Results 1/3
Number of farms enrolled and
geographical coverage
increased
Ratio: 1/3 sites SS/NSS
Proportion of SS and NSS, did
not change over time (p>0.05)
1. Demographics of enrollment

11. Results 2/3
2. Demographics of active participation (sharing)
Participation increased significantly
(p<0.001)
NSS less prone to report than SS
(but improved lately!)
Variability of participation higher
between than within counties (RE)

12. Results 2/3
2. Demographics of active participation (sharing)
Low probability of sharing
PRRS data during first half
(Jul12-Jun13)
High probability of sharing
PRRS cluster during
second half (Jul13-Jun14)
* Counties delimited have sites enrolled in RCP-N21
** Gradient per county indicates number of sites

13. Results 3/3
3. PRRS trend
A significant (p<0.001) monthly decrease of
PRRS incidence
Probability of sharing PRRS status
negatively associated with PRRS incidence
Density of large and medium-sized sites in
county positively related with PRRS
incidence
Proportion of vaccinated farms not
associated with the disease

14. Results 3/3
3. PRRS trend
Spatial and temporal distribution of pairs of incident cases

15. Results 3/3
3. PRRS trend
Spatial and temporal distribution of pairs of incident cases
• Temporal windows of < 3
weeks
• Spatial windows of <3
kilometers
1
5

16. 1. Three-stage systematic approach to evaluate the evolution of RCPs
(enrollment, sharing, incidence)
2. Farmers’ enrollment not necessarily a good estimate of participation
(may not share information on disease status)
3. Sharing information has increased, but NSS (~77%) less prone to report
(although it improved)
4. Sharing information among producers negatively correlated with disease
incidence
5. PRRS incidence has decreased, but spatial and temporal aggregations
remained over the study period
Discussion

17. 1. To apply this systematic approach to other RCPs (anybody
interested????)
2. To evaluate incentives and deterrence of participation in
RCPs and collaborative strategies to control PRRS at a
regional level
Future plans

18. Objective: To propose a methodological approach to
monitor emergence and spread of PRRSv strains
Study 2: Identify the emergence and
monitor the spread of new PRRSv strains
Moh Alkhamis, Andres Perez, Michael Murtaugh, Xiong Wang, Bob Morrison.
Applications of Bayesian Phylodynamic Methods in a Recent U.S. Porcine
Reproductive and Respiratory Syndrome Virus Outbreak. Frontiers in Veterinary
Science, submitted.

19. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Introduction
Suppose that we have sequences from 8 farms (1 per farm)
Each square represents one nucleotide of the sequence

20. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Introduction
Alignment

21. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Matrix of (nt) differences
1 2 3 4 5 6 7
2 2
3 1 2
4 5 4 4
5 2 1 1 3
6 4 3 3 2 2
7 3 1 3 4 2 4
8 6 5 5 1 5 3 5

22. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
(nt) Genetic distance
1 2 3 4 5 6 7
2 0.333
3 0.167 0.333
4 0.833 0.667 0.667
5 0.333 0.167 0.167
6 0.667 0.5 0.5 0.333 0.333
7 0.5 0.167 0.5 0.667 0.333 0.667
8 1 0.833 0.833 0.167 0.833 0.5 0.833
Genetic distance (GD)
GD = (nt, aa) differences / (nt, aa) compared
Probability of finding one (nt, aa) difference when two strains are
compared

23. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
1) Because sequences theoretically split into two descendant
sequences, phylogenetic trees are typically assumed to be
bifurcating
2) Topology: branching pattern of a tree
3) Taxa: any kind of taxonomic units (eg.: virus sequences)
Unrooted tree Rooted tree

24. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
If 4 taxa are being compared, there are 15 and 3
possible rooted and unrooted tree topologies,
respectively
From Nei and Kumar, 2000

25. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
When 10 taxa are being compared, the number of possible rooted topologies
is 34,459,425 and the number of possible bifurcating unrooted topologies is
2,027,025
Only one of them is the true topology
Reconstructed or inferred trees: trees built from observed sequences
Traditional methods to infer a tree (distance, parsimony, likelihood) ignore
the associated epidemiological information (space, time of identification,
associated data)

26. Correlation of alternative metrics of PRRSv relatedness, namely, difference (%) in
the number of nucleotides (X axis) and considering phylogenetic evolution (Y axis).
Correlation is quite bad

27. Phylogenetic methods
• We can use phylogenetic methods to infer:
– Most likely time of emergence of the strain;
– Past probability of spread between systems and
between type of farms
– Population (viral) size

28. Data
• 6,774 ORF 5 sequences
• January 1998 – April 2015
• 5 independent systems (coded A…E)
• Type of farm (sow, growing pig farms)
• Selected 1-7-4 PRRSv using a maximum likelihood
algorithm

29. Results
Some (n= 288) related
sequences (based on ML)
collected between
September 2003-March 2015
Most (n=241) are 1-7-4
sequences obtained between
Jan 2014 and Mar 2015

30. Results
Increase in
population size in
2014

31. Results
History of spread
between systems

32. Results
History of spread
between types of
farms

33. Dynamic representation

34. Future plans
• Continue enrollment of systems
• Standardization and optimization of protocols for data
collection and sharing
• Implementation of analytical tools into IT systems
• Provide near real time interpretation of results
• Contribute to PRRS control at a national scale

35. Thank you