Surveillance for invading plant pathogens · Surveillance for invading plant pathogens: Epidemic...
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Surveillance for invading plant pathogens: Epidemic modelling to quantify performance and optimise survey design
Stephen Parnell
Rothamsted Research, United Kingdom
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1. What will a surveillance program tell you?
2. How can we best target our sampling resources?
• Modelling & Epidemiology
Overview
Current applications:
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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?
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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?
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Citrus canker disease in urban Miami
4 study sites; 17973 trees Disease progress fully observed
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Miami Site 4
time (days)
0 200 400 600 800 1000 1200
incid
ence (
pro
port
ion infe
cte
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
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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?
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sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
• How well does the “rule of thumb” work?
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15
sample size (number of trees)
0 10 20 30 40 50 60
dete
ction-incid
ence q
*
0.00
0.05
0.10
0.15observed
Miami Site 1 Miami Site 2 Miami Site 3 Miami Site 4
rule of thumb
Early-warning surveillance: what will it tell you?
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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:
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Spatially-targeted sampling
Application to Citrus greening disease (HLB) in Florida
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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
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sample size (proportion of acreage sampled)
0.0 0.1 0.2 0.3 0.4 0.5
pro
port
ion o
bserv
ed-p
ositiv
e f
inds
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.
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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?
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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
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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
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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
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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
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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
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• 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
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• 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