Probabilistic Commodity
-Flow-Based Focusing of Monitoring Activities
Commodity-Flow-Based
to Facilitate Early Detection of Phytophthora ramorum Outbreaks
Steven
McKelvey
Steven McKelvey,
McKelvey, Associate
Associate Professor,
Professor, Department
Department of
of Mathematics,
Mathematics, Statistics
Statistics and
and Computer
Computer Science,
Science, Saint
Saint Olaf
Olaf College
College
Frank
Koch
Frank Koch,
Koch, Research
Research Assistant
Assistant Professor,
Professor, Department
Department of
of Forestry
Forestry and
and Environmental
Environmental Resources,
Resources, North
North Carolina
Carolina State
State University
University
National
National Forest
Forest Health
Health Monitoring
Monitoring Research
Research Team,
Team, Southern
Southern Research
Research Station,
Station, USDA
USDA Forest
Forest Service
Service
Bill
Smith
Bill Smith,
Smith, Quantitative
Quantitative Research
Research Ecologist,
Ecologist, National
National Forest
Forest Health
Health Monitoring
Monitoring Research
Research Team,
Team, Southern
Southern Research
Research Station,
Station, USDA
USDA Forest
Forest Service
Service
The
The work
work upon
upon which
which this
this poster
poster is
is based
based was
was funded
funded through
through an
an Evaluation
Evaluation Monitoring
Monitoring grant
grant (Project
(Project SO-R-08-01)
SO-R-08-01) awarded
awarded by
by the
the Southern
Southern Region,
Region, State
State and
and Private
Private Forestry,
Forestry, U.
U. S.
S. Forest
Forest Service
Service
Project
Project Objectives
Objectives
Products
Products
Through
Through the
the use
use of
of trace-forward
trace-forward information
information regarding
regarding the
the shipment
shipment of
of P.
P. ramorum
ramorum
infected
infected nursery
nursery stock
stock provided
provided by
by the
the USDA
USDA Animal
Animal and
and Plant
Plant Health
Health Inspection
Inspection
Service
Service (APHIS),
(APHIS), supplemented
supplemented by
by commodity
commodity flow
flow data
data from
from the
the US
US Departments
Departments of
of
Commerce
Commerce and
and Transportation,
Transportation, the
the analytical
analytical techniques
techniques and
and software
software developed
developed by
by
this
this project
project will
will allow
allow forest
forest health
health managers
managers to
to focus
focus their
their limited
limited resources
resources on
on areas
areas
with
with the
the greatest
greatest likelihood
likelihood of
of new
new P.
P. ramorum
ramorum infestation
infestation and
and thus
thus more
more quickly
quickly identify
identify
newly
newly infested
infested areas,
areas, increasing
increasing the
the likelihood
likelihood of
of successful
successful intervention
intervention before
before the
the
pathogen
pathogen crosses
crosses the
the urban-forest
urban-forest interface.
interface.
This
This project
project produced
produced two
two deliverables,
deliverables, aa technical
technical paper
paper describing
describing
the
the probabilistic
probabilistic techniques
techniques used
used in
in the
the analysis
analysis along
along with
with aa
comprehensive
comprehensive list
list of
of data
data sources.
sources. Examples
Examples of
of the
the application
application of
of
the
the model
model were
were also
also documented.
documented.
The
The second
second deliverable
deliverable was
was an
an open
open source
source software
software package,
package,
written
written in
in the
the Java
Java programming
programming language
language for
for portability
portability across
across
hardware
hardware and
and software
software platforms,
platforms, that
that implements
implements the
the model.
model. Full
Full
documentation
documentation of
of the
the software
software was
was provided.
provided.
Sample
Sample (Test)
(Test) Program
Program Output
Output
Parameter
Parameter File
File Name:
Name:
Node
Information
Node Information File
File Name:
Name:
Link
Information
File
Name:
Link Information File Name:
Mathematical
Mathematical Methodology
Methodology
july30.sdp
july30.sdp
july30.sni
july30.sni
july30.sfl
july30.sfl
----------------------------------------------------------------------------------------------------------------Newly
Newly discovered
discovered infected
infected site
site node
node IDs:
IDs:
The
The goal
goal of
of the
the probabilistic
probabilistic model
model and
and implementing
implementing software
software is
is to
to give
give the
the USDA
USDA Forest
Forest Service
Service an
an analytical
analytical tool
tool to
to
help
help focus
focus scarce
scarce inspection
inspection resources
resources on
on the
the early
early detection
detection of
of P.
P. ramorum
ramorum outbreaks
outbreaks in
in those
those parts
parts of
of North
North America
America
where
where the
the pathogen,
pathogen, which
which causes
causes Sudden
Sudden Oak
Oak Death
Death (SOD),
(SOD), isis not
not yet
yet endemic.
endemic. This
This isis accomplished
accomplished by
by using
using partial
partial
survey
survey results,
results, along
along with
with commodity
commodity flow
flow information,
information, to
to create
create an
an ordered
ordered list
list of
of those
those sites
sites presently
presently not
not known
known to
to be
be
infected.
infected. The
The list
list isis ordered
ordered by
by likelihood
likelihood of
of each
each site
site having
having recently
recently become
become infected
infected through
through the
the importation
importation of
of
infectious
infectious nursery
nursery stock.
stock.
The
The process
process of
of creating
creating this
this list
list consists
consists of
of several
several stages.
stages. In
In the
the first
first stage
stage some
some subset
subset of
of vulnerable
vulnerable sites,
sites, typically
typically
areas
areas east
east of
of the
the Rocky
Rocky Mountains,
Mountains, are
are surveyed.
surveyed. The
The surveyed
surveyed sites
sites are
are categorized
categorized as
as being
being recently
recently infected,
infected, very
very
likely
likely to
to be
be uninfected,
uninfected, or
or being
being aa site
site for
for which
which infection
infection status
status is
is uncertain.
uncertain. Sites
Sites with
with an
an uncertain
uncertain infection
infection status
status will
will
be
be treated
treated as
as though
though they
they were
were not
not surveyed.
surveyed. The
The combination
combination of
of newly
newly infected
infected sites
sites and
and recently
recently certified
certified clean
clean sites
sites
is
is called
called an
an infection
infection pattern.
pattern.
Once
Once newly
newly infected
infected and
and known
known clean
clean sites
sites are
are identified,
identified, known
known potential
potential sources
sources of
of infectious
infectious nursery
nursery stock
stock are
are
assigned
assigned probabilities
probabilities of
of being
being active
active sources
sources of
of infectious
infectious nursery
nursery stock.
stock. In
In the
the terminology
terminology of
of probability
probability theory
theory this
this isis aa
Bayesian
Bayesian process
process in
in which
which the
the probability
probability of
of infectious
infectious exports
exports assigned
assigned to
to each
each potential
potential source
source is
is updated
updated from
from some
some
previous
previous value
value based
based on
on the
the newly
newly observed
observed infection
infection pattern.
pattern. For
For example,
example, those
those sources
sources which
which happen
happen to
to send
send aa
large
large amount
amount of
of nursery
nursery stock
stock to
to newly
newly infected
infected destinations
destinations will
will be
be assigned
assigned aa high
high probability
probability of
of exporting
exporting infectious
infectious
materials
materials because
because the
the new
new infections
infections must
must have
have come
come from
from somewhere
somewhere and
and the
the sources
sources sending
sending materials
materials to
to these
these
destinations
destinations are
are good
good suspects.
suspects. Similarly,
Similarly, sources
sources that
that send
send large
large amounts
amounts of
of nursery
nursery stock
stock to
to sites
sites classified
classified as
as known
known
clean
clean sites
sites will
will be
be given
given aa low
low probability
probability of
of sending
sending infectious
infectious exports
exports because
because receiving
receiving these
these exports
exports has
has not
not resulted
resulted
in
in infection.
infection.
SODBuster
Opening
Window
66
88
Known
Known clean
clean site
site node
node IDs:
IDs:
55
99
Source
Source Infection
Infection Probabilities
Probabilities
Node
Node ID
ID
Node
Node Name
Name
11
22
33
44
2007
West-to-East
Nursery
Stock
Shipments
CA
CA Los
Los AA
CA
CA San
San DD
CA
CA Sacra
Sacra
CA
CA San
San JJ
AA priori
priori
Posterior
Posterior
0.1000
0.1000
0.2000
0.2000
0.3000
0.3000
0.4000
0.4000
0.0817
0.0817
0.9629
0.9629
0.4952
0.4952
0.6346
0.6346
------------------------------------------------------------------------------------------------------------------Destination
--Destination Nodes
Nodes Sorted
Sorted by
by node
node ID-ID-(*
(* indicates
indicates known
known infected
infected or
or known
known clean
clean node.)
node.)
ID
ID
5*
5*
6*
6*
77
8*
8*
9*
9*
Node
Node Name
Name
AL
AL Birmi
Birmi
FL
FL Jacks
Jacks
FL
Miami
FL Miami
MN
MN Minne
Minne
NY
New
NY New YY
P(Infected)
P(Infected)
0.0000
0.0000
1.0000
1.0000
0.3738
0.3738
1.0000
1.0000
0.0000
0.0000
------------------------------------------------------------------------------------------------------------------Destination
--Destination Nodes
Nodes Sorted
Sorted by
by node
node names-names-(*
(* indicates
indicates known
known infected
infected or
or known
known clean
clean node.)
node.)
Sample of
a Threat Map
(Model Test)
5*
5*
6*
6*
77
8*
8*
9*
9*
Node
Node Name
Name
AL
AL Birmi
Birmi
FL
Jacks
FL Jacks
FL
FL Miami
Miami
MN
MN Minne
Minne
NY
New
NY New YY
P(Infected)
P(Infected)
0.0000
0.0000
1.0000
1.0000
0.3738
0.3738
1.0000
1.0000
0.0000
0.0000
------------------------------------------------------------------------------------------------------------------Destination
--Destination Nodes
Nodes Sorted
Sorted by
by probability
probability of
of infection-infection-(*
(* indicates
indicates known
known infected
infected or
or known
known clean
clean node.)
node.)
ID
ID
Once
Once risks
risks have
have been
been assigned
assigned to
to the
the unsurveyed
unsurveyed destinations,
destinations, inspection
inspection resources
resources can
can be
be mobilized
mobilized to
to high
high risk
risk
destinations
destinations with
with the
the aim
aim of
of identifying
identifying those
those sites
sites that
that are,
are, in
in fact,
fact, infected
infected and
and taking
taking actions
actions to
to eliminate
eliminate the
the threat
threat of
of
introducing
introducing P.
P. ramorum
ramorum into
into forests
forests currently
currently free
free of
of the
the pathogen.
pathogen.
AL
AL Birmi
Birmi
NY
NY New
New YY
-----------------------------------------------------------------------------------------------------------------
ID
ID
After
After the
the probabilities
probabilities of
of exporting
exporting infectious
infectious materials
materials have
have been
been updated
updated attention
attention moves
moves to
to the
the unsurveyed
unsurveyed recipients
recipients
of
of nursery
nursery stock.
stock. For
For each
each unsurveyed
unsurveyed recipient
recipient of
of nursery
nursery stock,
stock, these
these are
are called
called destinations
destinations in
in what
what follows,
follows, aa
probability
probability is
is computed
computed that
that this
this site
site has
has become
become recently
recently infected.
infected. This
This probability
probability is
is based
based on
on two
two characteristics
characteristics of
of the
the
destination,
destination, from
from which
which sources
sources the
the destination's
destination's nursery
nursery stock
stock isis sent
sent and
and how
how much
much nursery
nursery stock
stock comes
comes from
from each
each
source.
source. IfIf aa given
given destination
destination receives
receives aa significant
significant amount
amount of
of its
its stock
stock from
from high
high risk
risk sources,
sources, that
that destination
destination will
will be
be
assigned
assigned aa relatively
relatively high
high probability
probability of
of infection.
infection. Conversely,
Conversely, ifif aa destination
destination receives
receives very
very little
little stock
stock from
from high
high risk
risk
sources,
sources, itit will
will be
be assigned
assigned aa low
low risk
risk of
of infection.
infection.
FL
FL Jacks
Jacks
MN
MN Minne
Minne
8*
8*
6*
6*
77
9*
9*
5*
5*
Node
Node Name
Name
MN
MN Minne
Minne
FL
Jacks
FL Jacks
FL
FL Miami
Miami
NY
New
NY New YY
AL
AL Birmi
Birmi
P(Infected)
P(Infected)
1.0000
1.0000
1.0000
1.0000
0.3738
0.3738
0.0000
0.0000
0.0000
0.0000