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

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Dave Hodson and Chris Gilligan

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

  1. 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. 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. 3. Global Wheat “Footprint”Rust Survey “Footprint” 2006Rust Survey “Footprint” 2012 • 13,000+ survey records • 30+ countries • large % of developing world wheat
  4. 4. Information from Surveys: Stem Rust Hotspots
  5. 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. 6. Information from Surveys: Yellow Rust Hotspots • Different distribution • More widespread than stem rust
  7. 7. 2009
  8. 8. 2010
  9. 9. 2011
  10. 10. 2012 • Ethiopia: Yellow rust hotspots very dynamic! • Why??
  11. 11. Ethiopia: Less food for rusts? 2010 Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
  12. 12. Ethiopia: Less food for rusts? 2012 Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
  13. 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. 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. 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. 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. 17. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Detected in UK in 2012
  18. 18. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival
  19. 19. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival ● Consider all potential sources 2008-2011
  20. 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. 21. Landscape scale models ● Chalara fraxinea „ Ash dieback ● Meteorological model „ risk of spore arrival ● Identify principal sources that pose risk
  22. 22. 2008 Landscape scale models ● Annual variation
  23. 23. 2009 Landscape scale models ● Annual variation
  24. 24. 2010 Landscape scale models ● Annual variation
  25. 25. 2011 Landscape scale models ● Annual variation
  26. 26. 2008 - 2011 Landscape scale models ● Cumulative risk
  27. 27. 2008 - 2011 Landscape scale models ● Model predictions independent of disease observations ● Very strong agreement ● Good predictor of arrival
  28. 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. 29. UK Spread Model: Infected Area 29 2014
  30. 30. UK Spread Model: Infected Area 30 2015
  31. 31. UK Spread Model: Infected Area 31 2016
  32. 32. UK Spread Model: Infected Area 32 2017
  33. 33. UK Spread Model: Infected Area 33 2018
  34. 34. UK Spread Model: Infected Area 34 2019
  35. 35. UK Spread Model: Infected Area 35 2020
  36. 36. UK Spread Model: Infected Area 36 2021
  37. 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. 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. 39. Wheat stem rust: 2) Density and connectivity of host Generating risk and hazard maps
  40. 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. 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. 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. 43. Acknowledgements Dr Matt Castle Rich Stutt James Cox Dr Nik Cunniffe Dr Stephen Parnell Dr Alex Archibald 43
  44. 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. 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. 46. BBSRC UK Research Councils UK Government & Industry International sponsors Acknowledgements

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