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