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Early Warning and Mitigation Planning: Epidemiological Models Add Value to Surveillance
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Early Warning and Mitigation Planning: Epidemiological Models Add Value to Surveillance

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Dave Hodson and Chris Gilligan

Dave Hodson and Chris Gilligan

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  • 1. Early warning and mitigation planning: Epidemiological models add value to surveillance D.P. Hodson1 & C.A. Gilligan2 1CIMMYT-Ethiopia 2Department of Plant Sciences, University of Cambridge, UK
  • 2. Overview: Partnerships adding value 1.  Surveillance Component „ Where are we now? „ Starting to add value to surveillance „ Foundation for epidemiological models 2.  Epidemiological Modelling Component „ How can epidemiological models help? ®  Predicting pathogen arrival and spread ®  ‘What if’ scenarios for management ®  Sampling strategies „ Data/information needs
  • 3. Global Wheat “Footprint”Rust Survey “Footprint” 2006Rust Survey “Footprint” 2012 • 13,000+ survey records • 30+ countries • large % of developing world wheat
  • 4. Information from Surveys: Stem Rust Hotspots
  • 5. Ug99 races, Hotspots & Wheat • Ug99 races detected in many hotspots (but not all) • Current stem rust hotspots occupy a tiny fraction of wheat area • What is the risk or hazard in those other wheat areas???
  • 6. Information from Surveys: Yellow Rust Hotspots • Different distribution • More widespread than stem rust
  • 7. 2009
  • 8. 2010
  • 9. 2011
  • 10. 2012 • Ethiopia: Yellow rust hotspots very dynamic! • Why??
  • 11. Ethiopia: Less food for rusts? 2010 Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
  • 12. Ethiopia: Less food for rusts? 2012 Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
  • 13. Ethiopia: Estimated Wheat Area Susceptibility to Ug99 races 2005/06 2013/14 BGRI Cornell Screening Dbase CIMMYT Wheat Atlas S MR/MS ? MR/MS MR MS S ?
  • 14. Early warning – Ethiopia 2013 Action Steps: • Informal rust planning meeting: 12th June 2013 (CIMMYT, EIAR, FAO) • Comprehensive Belg season surveys (EIAR/CIMMYT) • Formal rust planning meeting , 6th August 2013 (CIMMYT, EIAR, MoA Extension Directorate, ATA, FAO, Animal & Plant Health Directorate) • MoA, Extension Directorate + EIAR: Early, main season surveys Global Rust Monitoring System Assessment CWANA – Yellow Rust Outbreaks (surveys) Climatic Conditions – favourable for yellow rust?Regional Winds Rust Caution – May 17th
  • 15. Moving Forward: Value Addition from Epidemiological Models ● Good inputs = Good outputs „ Surveillance platform providing critical foundation layers: Host distribution, pathogen sources + environments, susceptibility distribution ● Despite an extensive surveillance network, many gaps remain e.g., where are the risks and hazards? Models have a key role here. ● Early warning. Some progress (e.g., Ethiopia 2013), but with model inputs can make substantial gains
  • 16. Epidemiological toolbox ● Landscape-scale models for disease spread ● Stochastic models: allow for uncertainty and variability ● Coupling meteorological with epidemiological models to predict: „ Risk – where might the pathogen arrive? „ Hazard – likely rates of spread if pathogen arrives? „ Control – ‘what if’ scenarios
  • 17. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Detected in UK in 2012
  • 18. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival
  • 19. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival ● Consider all potential sources 2008-2011
  • 20. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival ● Consider all potential sources 2008-2011 ●  Data supplied by UK Met Office ●  Computational analysis based on NAME: also tested HYSPLIT
  • 21. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival ● Identify principal sources that pose risk
  • 22. 2008 Landscape scale models ● Annual variation
  • 23. 2009 Landscape scale models ● Annual variation
  • 24. 2010 Landscape scale models ● Annual variation
  • 25. 2011 Landscape scale models ● Annual variation
  • 26. 2008 - 2011 Landscape scale models ● Cumulative risk
  • 27. 2008 - 2011 Landscape scale models ● Model predictions independent of disease observations ● Very strong agreement ● Good predictor of arrival
  • 28. UK Spread Model: Infected Area 28 2013 ● Epidemiological model „ Transmission „ Spread ®  Wind dispersal ®  Trade dispersal ● Host distribution „ Density, connectedness ● Environmental conditions „ Infection and sporulation S I D R Susceptible Infected Detected Removed
  • 29. UK Spread Model: Infected Area 29 2014
  • 30. UK Spread Model: Infected Area 30 2015
  • 31. UK Spread Model: Infected Area 31 2016
  • 32. UK Spread Model: Infected Area 32 2017
  • 33. UK Spread Model: Infected Area 33 2018
  • 34. UK Spread Model: Infected Area 34 2019
  • 35. UK Spread Model: Infected Area 35 2020
  • 36. UK Spread Model: Infected Area 36 2021
  • 37. UK Spread Model: Infected Area 37 2022 ●  Risk maps Where is invasion most likely? ●  Hazard maps Where is impact of spread most severe? ●  Inform control and sampling
  • 38. Wheat stem rust: 1) Long distance spore dispersal Meteorological dispersal model Integrate multiple sources of inoculum Very low probability of long distance dispersal Generating risk and hazard maps
  • 39. Wheat stem rust: 2) Density and connectivity of host Generating risk and hazard maps
  • 40. Wheat stem rust: 3) Environmental suitability Coincidence: Temp X Leaf wetness X Light Infection Sporulation Generating risk and hazard maps UK Met Office data @3-6h intervals
  • 41. Wheat stem rust: Generating risk and hazard maps ●  Hazard maps Where is impact of spread most severe? ●  Risk maps Where is invasion most likely?
  • 42. Wheat stem rust: Input from BGRI community ● Environmental suitability „ Infection „ Sporulation ● Host „ where when and how much? „ Alternative hosts ● Pathogen dispersal „ Data on dispersal „ Snapshots of disease maps Generating risk and hazard maps
  • 43. Acknowledgements Dr Matt Castle Rich Stutt James Cox Dr Nik Cunniffe Dr Stephen Parnell Dr Alex Archibald 43
  • 44. ● Sampling method varies depending on question „ First detection in new area „ How much disease is present at time of first detection „ Optimizing new detections after pathogen is introduced Optimising Sampling ● Use of epidemiological models for sampling „ Citrus greening in Florida „ Chalara fraxinea in UK „ Phytophthora ramorum in UK
  • 45. Optimising Sampling Chalara fraxinea again disease hazard map (potential outbreak size) xdistance to known outbreaks (probability of an outbreak) = risk weighting locations to sample
  • 46. BBSRC UK Research Councils UK Government & Industry International sponsors Acknowledgements

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