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
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
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
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?
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
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.
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