Download Quantitative approaches for early detection

Quantitative approaches for early
detection surveillance of invading
pests and diseases
Parnell S1, Mastin A1, Cunniffe NJ2, Gottwald TR3,
Gilligan CA2, van den Bosch F4
1University
of Salford, Manchester, UK
2University of Cambridge, Depn. Plant Sciences, UK
3USDA Agricultural Research Service, Ft. Pierce, Florida
4Rothamsted Research, Harpenden, UK
Early detection surveillance… (finding the
needle in the haystack)
Importance of early-detection for successful
pest/disease eradication or management. But…
1.
2.
What will a surveillance program/network
tell us?
How can we can best target our
surveillance to detect as early as possible?
Use of epidemiological modelling to
quantify probability of detection, informed
by the epidemiology.
Surveillance in the
wider environment
Applications: regulatory survey
Acute Oak Decline
P. ramorum
Ash dieback
Citrus canker
Citrus greening
Cassava brown streak
Stem rust Ug99
Plum pox virus
Xylella fastidiosa
Quantifying prevalence at first detection
When an invader is discovered for the first time, what prevalence
will it have reached?
Relating survey effort and epidemiology to quantify ‘detection-prevalence’
Mean prevalence on first detection
“rule of thumb” E(q*):
𝑬 𝒒∗ =
𝒓
𝑵/∆
=
𝒆𝒑𝒊𝒅𝒆𝒎𝒊𝒄 𝒈𝒓𝒐𝒘𝒕𝒉 𝒓𝒂𝒕𝒆
𝒔𝒂𝒎𝒑𝒍𝒊𝒏𝒈 𝒓𝒂𝒕𝒆
But does this rule of thumb
work?
Testing on Citrus greening
spatial & temporal data (also
tested on other systems)
Rule of thumb (CIs)
Observed (random sampling)
Observed (systematic sampling)
HLB epidemic data, Southern Gardens Citrus. Mike Irey and Tim
Gast.
Application to the current PPV survey
program in New York State
The USDA-APHIS PPV
Survey:
• Current sampling effort and
test sensitivity
o 56,415 commercial samples
& 5,235 residential trees
o ELISA test sensitivity 71%
o Survey conducted annually
• Estimates of the epidemic
growth rate
o Epidemic increases at rate
of 0.0015 per day
(proportion of the total
population)
Too much survey
effort?
Application of basic ideas to Oriental
chestnut gall wasp: (situation in June 2015)
Preliminary result: Based on an
estimation of volunteer survey effort,
what prevalence had it reached when
first discovered? (Nb heavy caveats…!)
Risk-based surveys for invading pests
and diseases
Smarter surveillance strategies:
Determining maps of epidemiological risk
and using to target survey efforts.
Risk-based surveys for invading pests
and diseases
Citrus greening example
locations to sample
probability of infection
X
hazard
(planting age & size)
=
Used in Florida since 2006 to search for multiple pathogens
to maximise new finds (Multi-Pest Survey)
Risk-based sample
Risk-based surveys for invading pests
and diseases
Chalara example
Hazard
X
Distance to
known positives
=
Sampling riskweighting
Nb Optimal course of action is not as simple as choosing N highest risk locations
Volunteer survey effort
Potential of modelling methods to help understand and target
volunteer survey efforts.
Quantitative approaches can help to answer:
– How many volunteers are enough and what will they tell us?
– Where should volunteer effort be targeted?
– Do we invest in more volunteers or training existing ones better?
(coverage vs efficiency)
– How can regulatory surveys best compliment volunteer efforts?
But some challenges…
– How do we measure volunteer survey effort (e.g. when there are no
“absence data”)?
– How do we measure volunteer detection-efficiency?
Acknowledgements
Co-authors:
Dr Frank van den Bosch
Dr Tim Gottwald
Dr Alex Mastin
Dr Nik Cunniffe
Prof Chris Gilligan
Rothamsted Research
US Department of Agriculture, ARS
University of Salford
University of Cambridge
University of Cambridge