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  • Surveillance for invading plant pathogens: Epidemic modelling to quantify performance and optimise survey design

    Stephen Parnell

    Rothamsted Research, United Kingdom

  • 1. What will a surveillance program tell you?

    2. How can we best target our sampling resources?

    • Modelling & Epidemiology

    Overview

    Current applications:

  • Insight from a simple epidemic model:

    Parnell et al. Journal of Theoretical Biology 305 (2012) 30–36

    D D D D Sample N hosts at regular intervals Δ

    Logistic growth with rate, r

    t0

    q*

    t*

    Early-warning surveillance: what will it tell you?

  • When an epidemic is discovered for the first time what is its incidence in the population (i.e. detection-incidence)?

    Mean detection-incidence is given by:

    How well does this “rule of thumb” work in practice?

    Early-warning surveillance: what will it tell you?

  • Citrus canker disease in urban Miami

    4 study sites; 17973 trees Disease progress fully observed

  • Miami Site 4

    time (days)

    0 200 400 600 800 1000 1200

    in c id

    e n c e (

    p ro

    p o rt

    io n i n fe

    c te

    d )

    0.00

    0.02

    0.04

    0.06

    0.08

    0.10

    0.12

    0.14

    0.16

    0.18

    Fit to logistic curve: epidemic growth rate, r = 0.014

  • q = 0.005 q = 0.008 q = 0.021 q = 0.064

    Detection! q*=0.064 (day 120)

    sampling round 1 sampling round 2 sampling round 3 sampling round 4

    Calculating detection-incidence q* from the data: • Simulate random sampling at regular intervals • Repeat thousands of times to get mean detection-incidence q*

    Nothing detected (day 30)

    Nothing detected (day 90)

    Nothing detected (day 60)

    Early-warning surveillance: what will it tell you?

  • sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

    0.10

    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

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    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

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    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

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    0.15

    • How well does the “rule of thumb” work?

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

    0.10

    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

    0.10

    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

    0.10

    0.15

    sample size (number of trees)

    0 10 20 30 40 50 60

    d e te

    c ti o n -i n c id

    e n c e q

    *

    0.00

    0.05

    0.10

    0.15 observed

    Miami Site 1 Miami Site 2 Miami Site 3 Miami Site 4

    rule of thumb

    Early-warning surveillance: what will it tell you?

  • 1. What will a surveillance program tell you?

    2. How can we best target our sampling resources?

    • Modelling & Epidemiology

    Overview

    Citrus disease Ash dieback Resistance Ug99 Cassava viruses

    Current applications:

  • Spatially-targeted sampling

    Application to Citrus greening disease (HLB) in Florida

  • potential consequences (planting age & size)

    probability of infection (distance to known outbreaks)

    = risk weighting (where to target samples)

    X

    Risk-based Sampling

    locations to sample

    Parnell et al. (2013) Ecological Applications. In Press.

    Application to Citrus greening disease (HLB) in Florida

  • sample size (proportion of acreage sampled)

    0.0 0.1 0.2 0.3 0.4 0.5

    p ro

    p o rt

    io n o

    b s e rv

    e d -p

    o s it iv

    e f

    in d s

    0.0

    0.2

    0.4

    0.6

    0.8

    1.0

    Practical output: used in Florida since 2006 to search for multiple pathogens (Multi-Pest Survey)

    Relative success compared to former strategy

    Risk-based Sampling

    Parnell et al. (2013) Ecological Applications. In Press.

  • Spatially optimised surveillance

    A single-run of the epidemic simulation (Individual based model)

    Average of thousands of runs of the epidemic simulation

    disease risk

    0

    1

    Where to sample to maximise the probability of early-warning?

  • Spatial optimisation

    Objective: (pre-invasion) Early warning surveillance

    Objective: (post-invasion) Maximising new disease finds

    Disease risk Optimal sample placement

    The answer depends on the question

    Solution: Risk-based sampling Solution: Widespread sampling

  • Spatial optimisation

    Residential trees Commercial trees

    • Individual based model of invasion and spread of HLB in Florida (Retrospective analysis!)

    • Estimate of citrus tree distribution at 1km resolution in Florida

    • Individual-based spread model

    Application to Citrus greening disease (HLB) in Florida

  • Spatial optimisation: HLB in Florida

    GFGF

    Mean disease risk of 1000 simulations

    Florida citrus distribution

    Residential trees Commercial trees

    GF

    10 highest risk sites

    10 optimal sites

    Disease entry

  • Including Risk of Entry:

    - Travel-census risk map

    (Tim Gottwald & Tim Riley, USDA)

    Incorporating into the method:

    1. Seed the models runs by probability of entry

    2. Run epidemic runs

    3. Identify optimal sample locations

    Spatial optimisation: HLB in Florida

  • Dr Francisco Laranjeira Rothamsted International Fellow

    Searching for citrus greening (HLB) in Brazil

    Transferred to Embrapa for use to inform regulatory surveillance for HLB in disease-free regions of Brazil

    Spatial Optimisation

  • • Take home messages

    Modelling can help to say what a surveillance

    program will actually tell you (quantification)

    With epidemiology and modelling we can find optimal surveillance designs

    Surveillance strategies need to be carefully matched to the specific objective

    Summary

  • • Dr Frank van den Bosch (Rothamsted Research)

    • Dr Tim Gottwald (USDA ARS)

    • Dr Nik Cunniffe (Cambridge University)

    • Prof Chris Gilligan (Cambridge University)

    • Dr Francisco Laranjeira (Embrapa, Brazil)

    • Tim Riley (USDA APHIS)

    Acknowledgements