Present and Future of the Dr. Morrison Swine Health Monitoring Program (MSHMP) - Dr. Andres Perez, Dr. Kim VanderWaal, College Of Veterinary Medicine, University of Minnesota, from the 2017 Allen D. Leman Swine Conference, September 16-19, 2017, St. Paul, Minnesota, USA. More presentations at http://www.swinecast.com/2017-leman-swine-conference-material

1. Dr. Morrison's Swine Health Monitoring
Program (MSHMP) for surveillance of
swine health conditions in the U.S.
Andres Perez
Professor, Department of Veterinary Population Medicine
Director, Center of Animal Health and Food Safety –OIE collaborating center
Endowed Chair of Global Animal Health and Food Safety
College of Veterinary Medicine
University of Minnesota
Leman Conference, August 2017

2. The MSHMP team
Faculty
• R. Morrison (deceased)
• A. Perez
• C. Corzo
• M. Torremorell
• J. Torrison
• J. Alvarez
• K. VanderWaal
• M. Alkhamis
Staff
• E. Geary
• C. Vilalta
• J. Sanhueza
• G. Machado
• P. Fioravante
• K. Kanankege
• Epi Interactive team

3. Dr. Morrison’s Swine Health Monitoring Program
(MSHMP)
Dr. Bob Morrison started the SHMP as an initiative to share data to
address, in part, some of those knowledge gaps
Objectives
1. Long term - develop capacity to give industry opportunity to
voluntarily respond to an emerging pathogen.
2. Short term - deliver value to producers and their veterinarians (such
that they participate for long term objective).
Data
• 2009 & 2010 - retrospective
• January 2011 – prospective
• Pathogen year: July 1  June 30

4. The MSHMP today
• 33 systems
– 26 companies
– 3 veterinary practice coordinated
systems
– 4 regional projects
• Participation
• 1,092 sow farms & 2.958 m sows.
• ~50% of U.S. sow population
• 25 companies and 4 regional
projects share logo and are willing to
share their premises IDs and
pathogen status in the interests of
national disease control

5. 5 things the
MSHMP
helped us to learn

6. 1. PRRS patterns at the national level

7. 2. Incidence varies among regions
and the TDR can predict the
emergence and decline of
epidemics

8. 3. PRRS impact on production

9. 4. Land slope, altitude, and coverage
influence PRRS risk after accounting
for swine density and size

10. 5. Sequencing can help to identify emerging strains, evaluate
spread between systems, and identify strain emergences

11. The MSHMP
moving forward

12. Forecasting PEDv breaks in sow
farms
Kim VanderWaal (kvw@umn.edu)
Andres Perez, Montse Torremorell, Gustavo Machado,
Carles Vilalta, Juan Sanhueza, Andres Perez

13. Data capture
Data capture
Wind
Etc.
Spatial data
Weather
Pig
movements
Visualize &
Analyze
Interpret
& Report
MSHMP data
Precipitation
/ Humidity
Quantify and dynamically predict the
risk of PED outbreaks in sow farms
• Likelihood of exposure from neighboring farms

14. PED outbreak?
Neighborhood effects on risk
Neighborhood effects:
• Outbreaks in neighboring farms
• Vaccination
• Swine farm density
• Animal movements
• Micro-environment (climate,
land use, vegetation, tree cover)

15. Neighborhood effects on risk

16. Preliminary results: PEDv
Accuracy=83.33%
Sensitivity=79.57%
Pos. Pred. Value = 83.8%
Random forest: Variable Importance

17. Week 4 Week 14

18. Looking ahead: Forecasting risk
• Forecast PRRS and PED breaks in sow farms in near real-time
» Biweekly, farm-level forecasts on the probability that
a sow farm will break
» Dynamic risk maps highlighting areas where new
breaks are likely given current conditions
» PRRS
Data capture
Data capture
Data
storage
Wind
Etc.
Pig
movements
Weather
MSHMP data
Data
prep
Visualize &
Analyze
Interpret
& Report
Spatial data
Precipitation
/ Humidity

19. Acknowledgments
• MSHMP participants
• Partners
– Iowa State University
– The Ohio State University
• Funding
– Swine Health Information Center
– National Pork Board
– MnDrive
– University of Minnesota