Preemptive Quarantine: Where Is It Warranted?
John R. Withrow,
1
Jr. ,
Eric L.
2
Smith ,
Frank
3
Koch ,
and Denys
4
Yemshanov
1Softec
Solutions, Inc., 384 Inverness Pkwy, Ste 211, Englewood, CO 80112
2USDA-FS FHTET, NRRC Bldg A, Ste 331, 2150 Centre Ave., Fort Collins, CO 80526
3USDA-FS Southern Research Station Forest Sciences Laboratory, P.O. Box 12254, Research Triangle Park, NC 27709
4Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, ON P6A 2E5
Abstract
In pest risk assessment it is frequently necessary to make time-critical
decisions regarding management and regulation of expanding pest
populations. In conditions where the invasive pest outbreak is expanding
rapidly, preemptive quarantine of the areas that are under imminent threat
of infestation is one of few available management tools that can be
implemented quickly to help control the expansion of the pest population.
The preemptive quarantine of locations that surround the infested area
also acts as a safeguard to mitigate a vexing issue of failed detections of
the pest in field surveys. We present here a method that assesses the
suitability of preemptive quarantine measures at the level of small
geographical subdivisions (U.S. counties). The cost of a preemptive
quarantine in a given subdivision is weighed against the protective benefit
to other neighboring subdivisions. We demonstrate the approach in the
development of a model to be used as a decision-aid tool in determining
the suitability of preemptive quarantine across multiple subdivisions
(counties) that surround the areas infested with the emerald ash borer
(EAB), an emerging threat of ash tree species in the North America. The
model identifies the U.S. counties where the introduction of preemptive
quarantine would most effectively slow the spread of the EAB population
and provide risk reduction to certain high-value areas.
Introduction
The procedure is a cost-benefit analysis
Infested Areas
COST
?
This area may
be a good
candidate for
preemptive
quarantine
Vulnerable High Value Area
Model Output – Fall, 2011
Figure 1
The management of large-scale outbreaks of invasive alien pests involves a
three-fold process of detection, quarantine, and either eradication or at
least suppression. Weaknesses in any of the three above categories
compromise the integrity of the process as a whole. Detecting the
presence of a new invasive organism is typically associated with a certain
degree of uncertainty. In addition, agencies tasked with the mandate of
surveying and regulating the invasive threats often have limited resources
and are unable to cover all the locations adjacent to the infested regions
with sufficient density of the surveillance plots. Hence, various predictive
models are employed to prioritize the locations where the arrival of the
invasive species is the most likely.
Since human-mediated spread has been recognized as an important vector
for many forest and agricultural pests, quarantine of the areas infested
with the invasive pest has been widely recognized as one of few available
measures to slow the spread of expanding pest populations. However,
imposing a quarantine is often a costly and irreversible action, and so
imposing a preemptive quarantine should only occur under conditions
highly favorable for doing so.
The final rating for each county is calculated as the difference between two
quantities – the benefit and the cost of quarantine. The benefit is
calculated as the probability that the county is infested multiplied by the
increased hazard to other counties, which is then multiplied by an
estimated effectiveness of quarantine, here assumed to provide a 50%
reduction in anthropogenic dispersal. The cost for the county is calculated
as simply the host value of that county multiplied by the probability that it
is NOT already infested.
The result is a single number calculated for each county, where positive
numbers indicate favorable conditions for preemptive quarantine, and
more positive numbers indicate a greater degree of quarantine urgency.
The benefit is the host value of surrounding
uninfested counties times the probability
that county A is infested times the
reduction in probability of spread that
comes from quarantining county A
Model Parameters
Estimate
30% annual increase
in counties infested
50% reduction
Description
Amplitude of natural and human-aided
dispersal in the absence of quarantine
Effectiveness of quarantine
Standard dispersal distance of human100 kilometers
aided dispersal
Standard dispersal distance of natural
5 kilometers
dispersal
All counties are
Host (hazard) value for each county
weighted equally
Amplitude of
human-aided
dispersal
Amplitude of
natural dispersal
These probabilities use
dispersal distance parameters
Benefit
minus
cost
Figure 1. Model results for the fall of 2011, shown with existing infested
counties in black and a preexisting preemptive quarantine boundary shown
in blue. Areas in red and orange have the largest suitability for being added
to the quarantine.
This modeling of dispersal allows for a probability-of-infestation value to
be calculated for each uninfested county. This probability, when multiplied
by the value of host in that county, gives a rating of hazard. In addition,
the dispersal modeling also allows for the calculation of how much this
hazard rating would increase for surrounding counties if the first county in
question were to be found as infested. Considering that detection efforts
are imperfect and that matters of quarantining EAB can be very urgent,
these probabilities play an important role in recommending areas for
preemptive quarantine.
The cost of preemptively quarantining
county A is the host value of the county A
times the probability that it is not already
infested by neighboring infested counties
(i.e., the probability that the quarantine is
actually preemptive)
This is the probability that county i is not already
infested, by either anthropogenic dispersal (first term)
or natural dispersal (second term). Each term is taken
as the complement of a summation of probabilities
that each infested county k might successfully disperse
into county i.
The model is applied to the management of emerald ash borer (EAB)
among counties in the eastern United States.
The number of counties infested with EAB has been found to grow at a rate
of about 30% per year. This provides an amplitude to the dispersal of EAB,
which is modeled as two omnidirectional Gaussian kernels emanating from
the centroid of each infested county, where two kernels are utilized to
separately depict anthropogenic and natural dispersal. Since the kernels
are Gaussian, each utilizes a “standard dispersal distance” that gives a
distance within which 68% of all dispersal is assumed to take place. These
distance parameters are estimated to be 100 km (anthropogenic) and 5 km
(natural dispersal).
A
BENEFIT
The Mathematics
In this study, we present a quantitative and objective methodology for
considering the cost of preemptive quarantine at a given geographical
subdivision and weighing it against the expected benefits of the
quarantine to the other neighboring subdivisions. The method takes the
form of a spatially-explicit decision-aid model that calculates a numeric
value to each subdivision. These values have utility both in an absolute
and relative (ranking) sense, with numbers greater than zero indicating
favorable conditions for preemptive quarantine, and more positive
numbers indicating greater degrees of quarantine urgency.
Methodology
vs.
A
Quarantine effectiveness
(0 – no effect, 1 – full effect)
Cost of preemptive
quarantine of county i
outweighs the benefit
Host value of county i
This term determines the total potential
harm that county i, if infested, could
potentially inflict on neighboring uninfested
counties j depending on their respective
host values Hj and the probability that an
infestation in county i could spread to
county j.
+
0
Benefit of preemptive
quarantine of county i
outweighs the cost
Results and Discussion
Figure 1 presents the map of the quarantine feasibility values for the scenario
that uses our estimated parameter values. The map illustrates several broad
predictions regarding the allocation of preemptive quarantine efforts for EAB
in eastern North America. The areas where such preemptive quarantine
would be most feasible are located in counties shown in red and orange. As
one might anticipate, these areas describe counties in close proximity to the
existing EAB infestations throughout the eastern U.S. with the most intense
values allocated to counties that are spatially surrounded by existing EAB
infested counties.
Suitability for preemptive quarantine (Q) is calculated for every county i. Simply put, it
is the difference between two terms, benefit and cost. The second term, cost, is simply
the host value of the county (Hi) multiplied by the probability that the county is not
already infested (Ii). The first term, benefit, is the probability that the county is
infested (1 – Ii) multiplied by the increase in risk that this infested county would now
enact on all surrounding counties (ϕ
ϕ) multiplied by the degree to which this risk is
reduced via quarantine (ω
ω).
Also shown in the figure is a boundary (shown in blue) of an existing planned
preemptive quarantine by USDA-APHIS in 2011. This boundary extends the
quarantine area to include all but extreme eastern Pennsylvania and all but
extreme southern West Virginia. It is observed that in these two states this
existing boundary is strongly approximated by the boundary between
moderate (yellow) and high (orange) values of model output. The model,
therefore, provides the prescriptive feedback of extending this existing
boundary of preemptive quarantine to include all areas of high (orange) and
very high (red) model output, along with the added value of a nationally
consistent objective basis for such quarantine.
Conclusions
A model is presented that for a given set of discrete spatial entities presents an
objective decision-aid for selectively employing a preemptive quarantine on one or
more spatial entities in an effort to further protect others from a spatially dispersing
exotic pest. The model has provided prescriptive value to the 2011 EAB quarantine
efforts of USDA-APHIS, and its simplistic formulation allows for intuitively reasonable
predictions, rapid data-driven feedback to changing conditions, and insights into which
ecological and sociological processes potentially play the most intensive role in the
ongoing process of predicting the dispersal of exotic pests and providing
recommendations as to management.
Acknowledgements
We are very grateful to Marla Downing of USDA-FHTET and Paul Chaloux of USDAAPHIS for their continued support of this project. Special thanks are extended to Ian
Leinwand for additional input and commentary. This project was funded under USDAFS Task Order AG-7604-D-09-0541.
EAB images courtesy chicagotribune.com, dec.ny.gov, good2golawncare.com, and emeraldashborer.info.