1. Introduction -Small scale models -Local vs. global impacts & risk-based culling: citrus canker -Prediction under uncertainty: Bahia bark scaling -Evidence-based policy: Dutch elm disease -Landscape scale models -Rapid response to an invading pathogen: ash dieback -“What if” scenarios: sudden oak death

2. Citrus canker -Small scale models -Local vs. global impacts & risk-based culling: citrus canker -Prediction under uncertainty: Bahia bark scaling -Evidence-based policy: Dutch elm disease -Landscape scale models -Rapid response to an invading pathogen: ash dieback -“What if” scenarios: sudden oak death

3. Citrus canker: using models to optimise controls 1)How can models be used to optimise control strategies? 2)How can the local and global impacts of disease be balanced? 3)Can we design dynamic control strategies? Underlying questions

4. Citrus canker Bacterial disease of citrus, caused by Xanthomonas axonopodis Affects most citrus cultivars Spreads by wind-borne rain Infection leads to - Poor fruit quality - Early fruit drop

5. Citrus canker: initial spread near Miami (1995-1998) 1995 1996 19971998 Entered Florida in 1995 (near Miami airport) Spread unchecked until 1998

6. Citrus canker: prophylactic removal (1998 - ) “125ft rule” (1998-2002) “1900ft rule” (2002-)

7. Citrus canker: control was abandoned in 2006 7

8. Citrus canker: why was control unsuccessful? 8

9. Citrus canker: learning from failure B1 B2 D1 D2 Monthly surveys Perfect for modelling

10. Citrus canker: B2 dataset

19. Citrus canker: individual based model Richard Stutt Prof. Chris Gilligan Dr Tim Gottwald (USDA) Webidemics: Web-based interactive demonstration of epidemiological modelling informing control strategies

20. Citrus canker: individual based model

21. Citrus canker: optimal cull radius and effect of delays 21 Optimal radius: 1)Balances number of trees lost to disease vs. control 2)Affected by parameter changes (e.g. delay before control)

22. 22 Citrus canker: balancing local vs. global impacts Epidemic Impact (EI) Epidemic Impact (EI) [Local removals] Epidemic Time (ET) [Global risk] Total Cost: EC = EI +  ET Optimal radius: Increases as more weight given to global impacts Just fail to control => epidemic lasts longer

23. Idea: assign time-dependent risk of further spread to each tree, based on infections observed thus far and estimates of how many it will be able to infect, and control trees which pose highest risk Many infections => High risk => More control Fewer infections => Less control Citrus canker: risk-based culling Sam Hyatt Twynam

24. 24 3) is simple (cut further where high R 0 ) and is staticComparing three controls 1)Constant radius 2)Risk-based 3)“Rule of thumb” based on R 0 24 Citrus canker: risk-based culling 2) is “complex” (and changes over time)

25. 25 Typical result (at each control’s optimum)Robustness to lack of knowledge Conclusions 1)Complex risk-based method can do very well (but not very transparent) 2)Approximation does quite well, easier to implement, and is robust to uncertainty 3) If level of knowledge very poor, constant radius difficult to beat 25 Citrus canker: risk-based culling

26. -Small scale models -Local vs. global impacts & risk-based culling: citrus canker -Prediction under uncertainty: Bahia bark scaling -Evidence-based policy: Dutch elm disease -Landscape scale models -Rapid response to an invading pathogen: ash dieback -“What if” scenarios: sudden oak death Bahia Bark Scaling

27. 1)Can useful predictions be made even when underlying epidemiology is uncertain? 2)How can models be used to optimise the economic aspects of cultural controls? Underlying questions Bahia bark scaling: predicting when there is uncertainty

28. 28 Dr Chico Laranjeira (Embrapa)

29. Bahia bark scaling: predicting when there is uncertainty 29 Markov Chain Monte Carlo (with data augmentation) Dr Chico Laranjeira Dr Franco Neri

30. 30 Results for a typical grower Model results 30 Bahia bark scaling: predicting when there is uncertainty Low planting density means fewer plants, but also less disease…modelling can calculate optimum spacing Scouting regularly improves roguing, but costs money… modelling can determine optimal schedule Interpretation