Dr. Bob Morrison, Dr. Carles Vilalta - Update from Swine Health Monitoring Project (SHMP)
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Update from Swine Health Monitoring Project (SHMP) - Dr. Bob Morrison, University of Minnesota; Dr. Carles Vilalta, University of Minnesota, from the 2016 North American PRRS Symposium, December 3‐4, 2016, Chicago, Illinois, USA. More presentations at http://www.swinecast.com/2016-north-american-prrs-symposium
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Dr. Bob Morrison, Dr. Carles Vilalta - Update from Swine Health Monitoring Project (SHMP)
- 1. Swine Health Monitoring Project December 3, 2016 Bob Morrison, P.I.
- 2. The SHMP team Faculty • R. Morrison • A. Perez • M. Torremorell • M. Alkhamis • K. VanderWaal • J. Alvarez and • D. Linhares • D. Holtkamp Students & postdocs • Andreia Arruda • Carles Vilalta • Emily Geary • Paulo Fioravante • Kaushi Kanankege • Gustavo Machado • Pablo Valdes • Gustavo De-Sousa-E-Silva
- 3. Current participation • Currently 29 systems with 1,022 sow farms & 2.63 m sows – 21 production companies – 4 veterinary practices – 4 regional projects • 22 and 4 projects share logo stating intentions • 300 non participants receive weekly report
- 4. SHMP Objectives • Long term – to develop capacity to support industry response to emerging pathogens. & • Short term – deliver value to encourage participation: – monitor disease incidence & prevalence. – supplement outbreak investigation. – understand implications of pig movement.
- 5. Our process
- 6. Weekly report • Page 1 – Participants • Page 2 – PRRS summary • Page 3 – PED summary • Page 4 – Senecavirus A etc. • Page 5 – Science page • Page 6 – Others receiving dated report • Page 7–xx – System reports
- 7. Publications • Tousignant et al. Temporal and spatial dynamics of porcine reproductive and respiratory syndrome virus infection in the United States. Am J Vet Res. January 2015, Vol. 76, No. 1, Pages 70-76. • Tousignant et al. Comparison between the 2013–2014 and 2009–2012 annual porcine reproductive and respiratory syndrome virus epidemics in a cohort of sow herds in the United States. Can Vet J 2015; 56:1087–1089. • Alvarez et al. Impact of PEDv on Performance of Growing Pigs. PLOS One. March 13, 2015 http://dx.doi.org/10.1371/journal.pone.0120532 • Goede et al. Production impact & time to stability in sow herds infected with porcine epidemic diarrhea virus (PEDV). Preventive Veterinary Medicine. Volume 123, 1 January 2016, Pages 202–207. • Alvarez et al. Spatial and temporal epidemiology of porcine epidemic diarrhea (PED) in the Midwest and Southeast regions of the United States. Prev Vet Med. Vol 123, 1 January 2016, Pages 155–160 • Valdes et al. 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. Feb 19, 2016 http://dx.doi.org/10.1371/journal.pone.0149498 • Alkhamis et al. Applications of Bayesian Phylodynamic Methods in a Recent U.S. Porcine Reproductive and Respiratory Syndrome Virus Outbreak. Front. Microbiol., 02 February 2016 http://dx.doi.org/10.3389/fmicb.2016.00067
- 8. Weekly science page receives positive feedback
- 10. PED Incidence
- 11. Tableau VDLs: (UMN – 6 systems) SQL db: PINs w/location Sequences ARC GIS Google Earth Visualization Alteryx: data cleaning Vet reports Today Excel
- 12. Advanced Analytics & Visualization Tableau VDLs: (UMN, ISU, SDSU etc) SQL db: PINs w/location Movement Sequences Feed deliveries ARC GIS Google Earth Visualization Alteryx: data cleaning Vet reports Eventually Performance
- 15. SHMP • Prevalence • Incidence • EWMA • TTS • Heat maps • ORF5 sequences • Susceptibility VDL
- 16. State IL State MN System 1 System 3
- 18. EWMA
- 20. Time To Stability (TTS)
- 21. Considerable difference in trend of TTS across SHMP systems
- 22. 1
- 23. 1
- 24. Shedding farms: Status 1, 2vx &2fvi
- 30. Related Studies
- 31. Should I build at top or bottom of hill. And do surrounding trees matter? • Objective: –Investigate whether land elevation and coverage are associated with PRRS breaks • While accounting for other important factors
- 33. Vehicles • Network analysis People • log Fomites Airborne • Weather & location Other • Manure removal Feed • Swab bins Outbreak Investigation - supplement Potential sources after ruling out pigs & semen
- 34. Outbreak investigation - supplement • 3 regions • What will we learn? – Proportion breaks associated with neighboring pig sites – Develop capacity and analytical approach to supplement current veterinarian’s investigation • What tools will we use? – Sequence similarity over time and space – Weather analysis – Was transport associated? – Transmission chain analysis
- 35. Can we detect new strains of PRRSv?
- 36. PRRS Management • 174 TTS & TTBP – Carl Betlach & Daniel Linhares • TTS at farrowing – Hunter Baldrey • TTS with wean down – Carles Vilalta and with ISU: • Using SHMP data to estimate cost (Derald) • Biosecurity assessment (Derald & Daniel) • SPC for early detection of PRRS break (Daniel)
- 37. Wean-down study • Where biosecurity is sufficient, PRRS virus dies out of sow herds (stability). • TTS at weaning is a function of litter status at birth and spread in lactation – TTS median at weaning is ~28 wks (Linhares et al, Betlach et al) – TTS median at farrowing is 13 weeks (Baldrey et al) • Ho: Decreasing prevalence by wean-down will decrease TTS if biosecurity is sufficient.
- 38. • Enroll 30 farms with matched controls • Protocol – Starting at 8 weeks post break, monitor prevalence of litters at 1-3 days – Vaccinate at processing at __ wks or __ prevalence – Max biosecurity 1-2 wk before wean-down (e.g. 0 foster) – Wean down to 10-14 days (younger is better) • Start monitoring at weaning • $1,000 credit / herd at achieving stability (BI)
- 39. SHMP – update summary • Critical issues: – Data security and confidentiality – Create value through data analysis and interpretation • Value for all can be gained from sharing data.
Editor's Notes
- Data visualization is an important part of our everyday process of managing data. As we all are big walking eyeballs visualize the data is important for two reasons, first it is going to help us to start understand the data, see if we can see any trend, cluster or other signs that can led us to analyze and extract the most of it in a better way. The second point is communication. Delivering information and data using charts and maps is most of the times the best way to make information more visually pleasant and more understandable. You can put a lot of data in one chart as we all know that a picture worth a thousand of words In order to do those previous points in the SHMP we are using a software that is called Tableau, specially built for data visualization.
- What is Tableau? What you can do? You can share it. Tableau is a drag and drop tool, that it can be connected directly to databases, such as SQL , or be connected in any data file in your computer with excel spreadsheets, csv or text files among others. This tool allows the user to do some filtering, integrated calculations and modifications while running the program without altering the original database. In this software you can combine variables and categories and create a wide range of visualizations, you can create scatterplots, bar charts, lines, pie charts, box plots and create also maps or filled maps and combine both maps and figures. In this visuals you can easily change the colors, sizes and add filters to those visualization to quickly aggregate and disaggregate the data to answer all the different questions that can come to your mind After seeing the data. And once you are done you can combine them in bigger presentations and link visualizations and databases between them and then with the same filter you can modify all the charts at the same time according to the filter. Finally, when you have finish you can upload them to the web and share it with other people.
- What is Tableau? What you can do? You can share it. Tableau is a drag and drop tool, that it can be connected directly to databases, such as SQL , or be connected in any data file in your computer with excel spreadsheets, csv or text files among others. This tool allows the user to do some filtering, integrated calculations and modifications while running the program without altering the original database. In this software you can combine variables and categories and create a wide range of visualizations, you can create scatterplots, bar charts, lines, pie charts, box plots and create also maps or filled maps and combine both maps and figures. In this visuals you can easily change the colors, sizes and add filters to those visualization to quickly aggregate and disaggregate the data to answer all the different questions that can come to your mind After seeing the data. And once you are done you can combine them in bigger presentations and link visualizations and databases between them and then with the same filter you can modify all the charts at the same time according to the filter. Finally, when you have finish you can upload them to the web and share it with other people.
- So let’s look at some examples of what we are doing with the data coming from the SHMP first and data coming directly from the lab. Prevalence over time. Colors and status. In this case, this visualization allow us to aggregate, disaggregate and compare the farm status that we receive weekly by State and System. In the two charts on top of the visualization we can easily compare how different is the prevalence from one state to another, and make questions like if they follow the same trend, how different are they in terms of percentage of farms in each status. Or if we think that they are regions that have similar characteristics we can aggregate them and compare how the Midwest region is doing compare with the East. And the same with the bottom ones, but in this case we can compare systems and their prevalence. See if those systems located in a specific region follow the same trend, or we can see the outcomes of different applied measures at a system level.
- Another example. We can build maps and charts and combine them with different filters. So in this case we can take incidence, as the number of cases and we can aggregate them on a map doing a simple heat map of number of cases per States. And using this map as a filter we can then extract informations such as the incidence curve or the aggregated by month and see that it is a different profile. And then ask questions and perform other analysis if this difference is due to density, climate, is it because differences in management. Or we can build other visualizations that can help us to elucidate those questions.
- In this case, this is the exponential weighted moving average of all the systems of the SHMP. the green dots are the cases and the blue line is the EWMA. Only looking at this charts some questions come right away: Is it seasonal? Is it repeatable? Is it decreasing? And if so why? And then you disaggregate the data to try to find the answers but what happens is the opposite you usually find more questions?
- And this is the result of the disaggregated EWMA. When we disaggregate the data we can see that in some of the regions there is a repeatable and seasonal pattern while in others it is not very clear. What happen with those years that incidence spiked more? Is it a new strain? Is because that season was colder, or more humid, or rained more?
- We can also calculate the time to stability with the data coming from the farm status. Time to stability is the time that takes to a farm from the break to reach what is called stability (in this case is 4 negative samplings in wean pigs). In this case we built a scatter plot of all the breaks that we have in our database and adding a trend line we can see if the tendency is to increase or decrease. We can filter per participants and compare their trends if they are improving over time or if differences in management could have an impact on the TTS. Then we can aggregate this data and benchmark
- And compare the mean values for each system
- Or plot those values in an histogram and define the distribution and look for those farms that are out of normality and help them to reduce their TTS.
- Last example of data coming from the data we are gathering weekly. With data coming from SHMP we can aggregate the data and create heat maps of farms that could be shedding, status 1 and farms using vaccine and life virus as a control measures
- Pork lunch?