Plant disease epidemiology

2. Why plant disease epidemiology? Importance of plant diseases because of epidemics Need to understand epidemics – as we know them today to implement effective control tools (incl. HPR) to deploy efficient, durable control tools (ditto) Need to predict epidemics – which will inevitably occur (possibly new diseases) – tomorrow climate change (incl. water scarcity) labor resource, natural resources, energy shortage drivers of agricultural change affect crop health: (1) the relative importance of diseases is changing, (2) factors underpinning epidemics are changing—mechanisms remain Many diseases: need for a framework, for methodology New approaches (general theory, R0)

3. Plant disease epidemiology can Help control epidemics (good) host plant resistance - 1 (partial HPR) host plant resistance - 2 (deployment: complete HPR, partial HPR; over space and/or time) tactical decisions: crop (health) management Help prevent epidemics (better) host plant resistance - 3 (complete HPR) disease exclusion techniques (e.g., seed health) Provide a conceptual framework: so many diseases, some barely known, biologically

4. seven questions Why do some diseases take off, whereas others do not? Why do some strains, races, or pathotypes die out, some coexist, and others come to dominate pathogen populations? How does the inherent variability associated with epidemics translate into risk? Given that new infections occur at the small scale but epidemics are manifest at the large scale, how can we scale from individual to population behavior? How can this information be used to identify control methods? How can this information be used to optimize the efficient deployment and durability of control methods? How does the way we grow and protect our crops or manage our natural and seminatural environment affect these outcomes?

6. Shapes of a few rice disease epidemics

7. the SEIR model SEIR = Suscepts, Exposed, Infectious, Removed or (Plant Pathology): H, Healthy sites L, Latent sites I, Infectious sites, and P, post-infectious sites One key rate: infection rate Two delays: latency period, infectious period

8. the SEIR model: applications medical epidemiology measles HIV influenza tuberculosis animal epidemiology Pseudorabies virus in pigs Mouse typhoid computer viruses? … and botanical epidemiology

9. infection rate (RI) - equation dL/dt = RI = Rc I Ca L: latent sites; I: infectious sites Rc: basic infection rate corrected for removals = number of new infections, per unit time, per infectious site (I) C: “correction factor” = fraction of healthy sites (H), relative to the total number of sites in the system a: disease aggregation coefficient

10. infection rate (RI) – over time dL/dt = RI = Rc I Ca

11. some additional ‘detail’ growth of the host = crop growth = growth of healthy sites senescence = physiological (or/and) pathological additional effects (on rate of infection only): plant age (variable susceptibility) temperature canopy moisture

12. spatial scales of plant disease epidemics

13. scaling the model structure to address different diseases definition for a site (a lesion) sites: levels of hierarchy chosen: portion of leaf area: leaf blast, brown spot a leaf: bacterial blight a tiller: sheath blight a plant: tungro

14. the SEIR model in plant pathology

15. the SEIR model in plant pathology

16. SEIR – system of differential ordinal equations H : number of healthy individuals L : number of latent individuals I : number of infectious individuals R : number of post-infectious (removed) individuals 1/? : mean latent period 1/µ : mean infectious period ß : per capita transmission rate (new diseased individuals per diseased individual per healthy individual per unit time).

17. A few rice disease epidemics simulated

18. A few rice disease epidemics simulated

19. From genes to landscapes: epidemiological concepts bridging host plant resistance concepts

20. Simulated effects of components of partial resistance to leaf blast

21. Simulated effects of components of partial resistance to leaf blast

22. Simulated effects of aggregation on sheath blight epidemics

23. Simulated effects of aggregation on sheath blight epidemics

24. Simulated effects of onset time on tungro epidemics

25. Simulated effects of onset time on tungro epidemics

26. Potential epidemics

29. invasion and persistence invasion: the potential for a pathogen to cause an epidemic persistence: the ability a pathogen may have to survive over successive host cycles (endemicity; polyetic processes)

30. Perspectives (invasion = I, persistence = P) I: crop health management with (variable, evolving) crop management crop health as a whole – multiple diseases I + P: optimize disease management (control points) HPR; crop management; variable spatial scales (plot? field?agric. landscape), depending on disease variable temporal scale (crop stage?season?multiple seasons), depending on disease P: emerging diseases (FSm, Viruses, Spikelet Rot Disease) specifc disease often: important (biological) knowledge gaps I + P: anticipatory research: climate change and disease epidemics potential epidemics link with large-scale characterization, global ag. change & natural resource management outcome: breeding priorities I: design of durable resistance in environmental contexts where HPR can be sustained design of reliable screening procedures landscapes (different scales) for control