Dr. Amy Kinsley - Movement Matters: Using Swine Shipment Patterns to Identify Farms For Targeted Disease Surveillance and Control
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Movement Matters: Using Swine Shipment Patterns to Identify Farms For Targeted Disease Surveillance and Control - Dr. Amy Kinsley, from the 2018 Allen D. Leman Swine Conference, September 15-18, 2018, St. Paul, Minnesota, USA. More presentations at http://www.swinecast.com/2018-leman-swine-conference-material
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Dr. Amy Kinsley - Movement Matters: Using Swine Shipment Patterns to Identify Farms For Targeted Disease Surveillance and Control
- 1. Movement Matters: Using Swine Shipment Patterns to Identify Farms For Targeted Disease Surveillance and Control Amy C. Kinsley, DVM, PhD Meggan Craft, PhD Andres Perez, DVM, PhD Kim VanderWaal, PhD
- 2. 1. Describe movement patterns in swine production systems-two regions of the U.S. 2. Describe and discuss results of analyses in which we used movement data to better understand how we can identify farms which may be targeted to increase the efficacy of infectious disease control strategies. Today’s discussion
- 3. Contact is a key determinant of transmission of infectious diseases. Because animal traceability in the US is limited, there is a lack of knowledge about the location and movement of animals in livestock systems. Infectious disease epidemics in livestock are important This is especially true in swine whose production is coordinated around stages of production. Infectious diseases can have devastating social, economic, and environmental impacts.
- 4. Each farm has an equal chance of mixing with every other farm in study. Homogenous mixing within a defined geographical area. Pomeroy et al., Transboundary and Emerging Diseases (2015) Transmission is scaled inversely with distance from infected farm. All means of contact that can cause disease transmission that is spatially independent. Contact can be described in different ways
- 5. Each farm has an equal chance of mixing with every other farm in study. Homogenous mixing within a defined geographical area. Pomeroy et al., Transboundary and Emerging Diseases (2015) Transmission is scaled inversely with distance from infected farm. All means of contact that can cause disease transmission that is spatially independent. Contact can be described in different ways
- 6. Network Analyses Network analysis-a method to look at relationships through mathematical graphs www.kateto.net
- 7. Node-(farm, site: unique premise ID, unique coordinate value) Edge-contact (shipment of pigs) Network Analyses
- 8. October 2013- October 2014 May 2012- May 2013 April 2013- April 2014
- 9. Degree-quantify the contact between farms A B A=1 B=2
- 11. Trends: 1. Finishing farms have higher in-degrees than out-degrees. 2. Nursery farms have higher out-degrees than in-degrees. Is this in line with your expectations? Why or why not?
- 12. Shweta Bansal et al. (2010)
- 13. 50 day snapshots for the entire year Shweta Bansal et al. (2010) Noremark et al. BMC Vet Research (2014)
- 15. 𝑅𝑜 = 𝐷 ∗ 𝑝 ∗ 𝑐 D = duration of infectiousness p=probability of infection given contact c=average contact rate Vulnerability of a system to disease spread R0- “R nought”- The number of secondary infections an infected person (or farm) in a completely susceptible population. In the beginning of an outbreak in which there is no immunity Take-away points of the equation: 1. R0 is a function of properties specific to the pathogen and to the population’s contact rate (contact necessary for disease transmission) 2. These two components can be separated and quantified
- 16. Vulnerability of a system to disease spread
- 18. Can we use these metrics to target farm in order to reduce vulnerability? “Removing” farms can potentially decrease vulnerability to disease spread
- 19. Can we use these metrics to target farm in order to reduce vulnerability? “Removing” farms can potentially decrease vulnerability to disease spread
- 21. • Biosecurity • We only focused on movement-based transmission • Movement patterns change over time-one year might not represent the next Limitations
- 22. Advisors and collaborators: Andres Perez, Meggan Craft, Kimberly VanderWaal Funding Sources: Samuel Maheswaran Fellowship Acknowledgements
Editor's Notes
- Points within to solicit feedback-questions-what would expect for farms with the highest MIP ( have two or three points)
- Will we use all 3 production systems?
- Because So we know that for livestock populations, infectious disease transmission occurs through three main routes. Simplify-we don’t know anything about contact—location, movement, etc we know this is essential for tx
- Spatial representation of FMDV transmission. Multiple methods have been used to capture FMDV transmission among farms. Some models assume homogeneous mixing, implying high rates of mixing in the host population (Garner and Lack, ; Haydon et al., ; Bouma et al., ; Tsutsui et al., ; Ap Dewi et al., ; Kobayashi et al., ). Other models assume local interactions (Doran and Laffan, ; Ward et al., ; Highfield et al., ) or kernel‐weighted spread (Keeling et al., ; Chis Ster and Ferguson, ), indicating that transmission is based on the distance between susceptible and infectious farms. Finally, network models provide an alternative representation, in which the contact structure is based on multiple factors (Green et al., ; Kiss et al., ; Kao et al., ). IF THIS IMAGE HAS BEEN PROVIDED BY OR IS OWNED BY A THIRD PARTY, AS INDICATED IN THE CAPTION LINE, THEN FURTHER PERMISSION MAY BE NEEDED BEFORE ANY FURTHER USE. PLEASE CONTACT WILEY'S PERMISSIONS DEPARTMENT ON PERMISSIONS@WILEY.COM OR USE THE RIGHTSLINK SERVICE BY CLICKING ON THE 'REQUEST PERMISSIONS' LINK ACCOMPANYING THIS ARTICLE. WILEY OR AUTHOR OWNED IMAGES MAY BE USED FOR NONTO PROPER CITA
- Spatial representation of FMDV transmission. Multiple methods have been used to capture FMDV transmission among farms. Some models assume homogeneous mixing, implying high rates of mixing in the host population (Garner and Lack, ; Haydon et al., ; Bouma et al., ; Tsutsui et al., ; Ap Dewi et al., ; Kobayashi et al., ). Other models assume local interactions (Doran and Laffan, ; Ward et al., ; Highfield et al., ) or kernel‐weighted spread (Keeling et al., ; Chis Ster and Ferguson, ), indicating that transmission is based on the distance between susceptible and infectious farms. Finally, network models provide an alternative representation, in which the contact structure is based on multiple factors (Green et al., ; Kiss et al., ; Kao et al., ). IF THIS IMAGE HAS BEEN PROVIDED BY OR IS OWNED BY A THIRD PARTY, AS INDICATED IN THE CAPTION LINE, THEN FURTHER PERMISSION MAY BE NEEDED BEFORE ANY FURTHER USE. PLEASE CONTACT WILEY'S PERMISSIONS DEPARTMENT ON PERMISSIONS@WILEY.COM OR USE THE RIGHTSLINK SERVICE BY CLICKING ON THE 'REQUEST PERMISSIONS' LINK ACCOMPANYING THIS ARTICLE. WILEY OR AUTHOR OWNED IMAGES MAY BE USED FOR NONTO PROPER CITA
- Basics of network analyses-how they have been used in animal movement before.
- Add Dates- what we assumed-removed slaughter facilities etc.
- Highlight an example
- The dynamic nature of contact networks in infectious disease epidemiology Shweta Bansal, Jonathan Read, Babak Pourbohloul & Lauren Ancel Meyers https://doi.org/10.1080/17513758.2010.503376 (2010)
- The dynamic nature of contact networks in infectious disease epidemiology Shweta Bansal, Jonathan Read, Babak Pourbohloul & Lauren Ancel Meyers https://doi.org/10.1080/17513758.2010.503376 (2010)
- vulnerability of network (production system) to disease spread
- vulnerability of network (production system) to disease spread
- Removing removing nodes from the network-surveillance and removing with variation in degree. Remove the highest MIp-how does it lead to decreases
- Removing removing nodes from the network-surveillance and removing with variation in degree. Remove the highest MIp-how does it lead to decreases
- SHIC, USDA