Id tif i hi hl t d ti t f li it tiIdentifying highly connected counties compensates for resource limitationsIdentifying highly connected counties compensates for resource limitationsIdentifying highly connected counties compensates for resource limitationsy g g y ph li ti l d f i i thwhen sampling national spread of an invasive pathogenwhen sampling national spread of an invasive pathogenwhen sampling national spread of an invasive pathogenp g p p g

S S t 1 2 C S li 2 S A I d3 J M S H t hi 4 d K A G tt1 (k tt@k d )S Sutrave1,2 C Scoglio2 S A Isard3 J M S Hutchinson4 and K A Garrett1 (kgarrett@ksu edu)S. Sutrave , , C. Scoglio , S. A. Isard , J. M. S. Hutchinson , and K. A. Garrett (kgarrett@ksu.edu), g , , , ( g @ )1D t t f Pl t P th l 2D t t f El t i l d C t E i i 4D t t f G h K St t U i it1Department of Plant Pathology 2Department of Electrical and Computer Engineering 4Department of Geography Kansas State UniversityDepartment of Plant Pathology, Department of Electrical and Computer Engineering, Department of Geography, Kansas State University

3Departments of Plant Pathology and Meteorology Pennsylvania State University3Departments of Plant Pathology and Meteorology, Pennsylvania State UniversityDepartments of Plant Pathology and Meteorology, Pennsylvania State University

Ab t t R ltAbstract ResultsAbstract Results

Surveying invasive pathogens is resource intensive but ff fSurveying invasive pathogens is resource intensive, but The different methods for selecting sampling sites are listedy g p g ,k f f l ti t t t i W d l d

The different methods for selecting sampling sites are listed key for formulating management strategies. We developed here in increasing order of performancekey for formulating management strategies. We developed here in increasing order of performancea dynamic network model for US soybean epidemics with

ga dynamic network model for US soybean epidemics, with counties as nodes and link weights a function of host 4 R d l ticounties as nodes and link weights a function of host 4. Random selectionghectarage and wind speed and direction

4. Random selectionhectarage and wind speed and direction. g p

3 Zonal selection (based on more heavily weighting3. Zonal selection (based on more heavily weighting We used the model to compare four strategies for selecting

( y g gregions nearer the south where the pathogen overwinters)We used the model to compare four strategies for selecting regions nearer the south, where the pathogen overwinters)

an optimal subset of sentinel plots listed here in order ofg , p g )

an optimal subset of sentinel plots, listed here in order of p pincreasing performance: random selection zonal selectionincreasing performance: random selection, zonal selection 2 Frequency-based selection (based on how frequently theg p ,(b d h il i hti i th

2. Frequency-based selection (based on how frequently the (based on more heavily weighting regions nearer the county had been infected in the past)(based on more heavily weighting regions nearer the county had been infected in the past)south where the pathogen overwinters) frequency-based

y p )Figure 1 An example of model predictions for countysouth, where the pathogen overwinters), frequency-based Figure 1. An example of model predictions for county

selection (based on how frequently the county had been 1 F b d l ti i ht d b th dg p p yd t ti t G d b dselection (based on how frequently the county had been 1 Frequency-based selection weighted by the nodenodes at one time step. Green nodes are observed or( q y y

infected in the past) and frequency based selection1. Frequency based selection weighted by the node nodes at one time step. Green nodes are observed or

hi hl lik l t l k i f ti d d b dinfected in the past), and frequency-based selection strength (sum of weighted edges) of the sentinel plot in thehighly likely to lack infection red nodes are observed orec ed e pas ), a d eque cy based se ec oi ht d b th d t th f th ti l l t i th

strength (sum of weighted edges) of the sentinel plot in the highly likely to lack infection, red nodes are observed or weighted by the node strength of the sentinel plot in the network modelhighly likely to be infected and nodes with darker shadesweighted by the node strength of the sentinel plot in the network model.highly likely to be infected, and nodes with darker shades network model of yellow or orange have relatively higher likelihood ofnetwork model. of yellow or orange have relatively higher likelihood of

R d i th ti l l t t t 10% f th i i l ty g y g

i f ti Reducing the sentinel plot set to 10% of the original setinfection.Wh d i t k ti f i i i

Reducing the sentinel plot set to 10% of the original set infection.When dynamic network properties for invasive species are increased the typical error to 20% under random selectionWhen dynamic network properties for invasive species are h t i d thi i f ti b d t d th

increased the typical error to 20% under random selection, characterized this information can be used to reduce the but only 6% under zonal selection (Fig 2) When thecharacterized, this information can be used to reduce the but only 6% under zonal selection (Fig. 2). When the resources necessary to survey and predict invasion

y ( g )sentinel plot set as red ced to 2 5% of the original setresources necessary to survey and predict invasion

M th d sentinel plot set was reduced to 2.5% of the original set, progress Methods se t e p ot set as educed to 5% o t e o g a set,

t i l f f b d l ti 10%progress. Methods typical errors for frequency-based selection were 10%Networkp g typical errors for frequency based selection were 10%, Networkwhile for frequency-based selection weighted by nodeNode while for frequency-based selection weighted by node Node

Ed We developed a dynamic network model for US soybean strength typical errors were only 5% (Fig 3)Edge We developed a dynamic network model for US soybean strength typical errors were only 5% (Fig. 3). g y yepidemics with counties as nodes and link weights a

g yp y ( g )epidemics, with counties as nodes and link weights a p , gf ti f h t h t d i d d d di ti D l i d t il d i f ti b t id i t k ifunction of host hectarage and wind speed and direction. Developing detailed information about epidemic networks isfunction of host hectarage and wind speed and direction. Developing detailed information about epidemic networks is We used the soybean sentinel network data to estimate costly at the outset but provides a basis for cost savings inWe used the soybean sentinel network data to estimate costly at the outset, but provides a basis for cost savings in parameters in the network model The network model the futureEd i ht L l f i t ti b t th i parameters in the network model. The network model the future. Edge weight: Level of interaction between the pair pframework supports evaluation of the predictions based

g g pof nodes framework supports evaluation of the predictions based of nodes

D i t Ed i ht h tipp p

i f ti f diff t b t f th d hDynamic nature: Edge weights change over time.

on information from different subsets of the nodes wherey g g g

on information from different subsets of the nodes where soybean rust incidence data were available

I t d tisoybean rust incidence data were available.

IntroductionIntroductionIn simulations we evaluated the performance of theIn simulations, we evaluated the performance of the , p

d l f di ti id i hmodel for predicting epidemic progress whenS i i i th b hi hl

model for predicting epidemic progress when Surveying invasive pathogens can be highly resource progressively more sentinel plots were removed WeSurveying invasive pathogens can be highly resource progressively more sentinel plots were removed. We intensive yet near-real-time evaluations of invasion compared the performance for four different strategies forintensive, yet near-real-time evaluations of invasion compared the performance for four different strategies for progress are important resources for management

p p gdetermining which sentinel plots to maintainprogress are important resources for management determining which sentinel plots to maintain.p g p g

planning In the case of the soybean rust invasion of theg p

planning. In the case of the soybean rust invasion of the p g yU it d St t f t i it i t tUnited States use of an extensive monitoring system toUnited States, use of an extensive monitoring system to demarcate inoculum source areas an aerobiologicaldemarcate inoculum source areas, an aerobiological

Figure 3 Summary of infection frequency based selectionmodel to predict progress of the invasion and a rapid Figure 3. Summary of infection frequency based selection model to predict progress of the invasion, and a rapid g y q yd i ht d i f ti f d d t th

p p g pcommunication network saved U S soybean growers and weighted infection frequency and node strengthcommunication network saved U.S. soybean growers and weighted infection frequency and node strength y g

i t l $200 M/ M d li f f t t f based selection (weighted in the ratio 80:20) over all theapproximately $200 M/yr Modeling of future movement of based selection (weighted in the ratio 80:20) over all the approximately $200 M/yr. Modeling of future movement of years Red plot indicates results of infection frequencythe pathogen (Phakopsora pachyrhizi) was based on data years. Red plot indicates results of infection frequency the pathogen (Phakopsora pachyrhizi) was based on data y p q ybased selection blue plot indicates results of weightedabout current disease locations from an extensive network based selection, blue plot indicates results of weighted about current disease locations from an extensive network , p gi f ti f d d t th b d l tiof sentinel plots infection frequency and node strength based selection.of sentinel plots. infection frequency and node strength based selection. o se t e p otsAddition of node strength information to the infectionAddition of node strength information to the infection frequencies of the nodes lowers the errors significantlyObjectives frequencies of the nodes lowers the errors significantly. Objectives q g yNote that the range of errors depicted is narrower than inObjectives Note that the range of errors depicted is narrower than in g pFi 2Figure 2Figure 2.

Fi 2 S f th f f ti l l t1 Develop a dynamic network model for soybean rust in the A k l d tFigure 2 Summary of the performance of sentinel plot1. Develop a dynamic network model for soybean rust in the AcknowledgementsFigure 2. Summary of the performance of sentinel plot USA using the sentinel plot dataset and local information

Acknowledgementssubsets determined using random selection and zonalUSA, using the sentinel plot dataset and local information subsets determined using random selection and zonal , g pb t h t il bilit d i d d d di ti selection over all the years Red plots indicate results ofabout host availability and wind speed and direction We appreciate support of this work by USDA NC RIPM Grant 2010-selection over all the years. Red plots indicate results of about host availability and wind speed and direction We appreciate support of this work by USDA NC RIPM Grant 2010

34103 20964 USDA APHIS Grant 11 8453 1483 CA a USDA APHISy p

d l ti bl l t i di t lt f l 34103-20964, USDA APHIS Grant 11-8453-1483-CA, a USDA APHIS random selection, blue plots indicate results of zonal2 Apply this model to evaluate a set of strategies for Grant for spatial modeling, NSF Grant EF-0525712 as part of the joint

random selection, blue plots indicate results of zonal l ti St t i l l ti i l2. Apply this model to evaluate a set of strategies for Grant for spatial modeling, NSF Grant EF 0525712 as part of the joint

NSF NIH Ecology of Infectious Disease program NSF Grant DEBselection Strategic zonal selection gives lower errorssampling invasive movement under increasing limits on NSF-NIH Ecology of Infectious Disease program, NSF Grant DEB-selection. Strategic zonal selection gives lower errors sampling invasive movement under increasing limits on 0516046, NSF Grant SBE-0244984 (R. A. Dyer, PI), and the Kansascompared to random selectionp g gsampling reso rces

0516046, NSF Grant SBE 0244984 (R. A. Dyer, PI), and the Kansas Agricultural Experiment Station (Contribution No 12 197 J)

compared to random selection.sampling resources Agricultural Experiment Station (Contribution No. 12-197-J).sa p g esou ces