Developing GIS-based eastern equine - BioMed Central

RESEARCH Open Access

Developing GIS-based eastern equineencephalitis vector-host modelsin Tuskegee, AlabamaBenjamin G Jacob1*, Nathan D Burkett-Cadena2, Jeffrey C Luvall3, Sarah H Parcak4, Christopher JW McClure5,Laura K Estep5, Geoffrey E Hill5, Eddie W Cupp6, Robert J Novak1, Thomas R Unnasch7

Abstract

Background: A site near Tuskegee, Alabama was examined for vector-host activities of eastern equineencephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2® from QuickBird dataencompassing visible and near-infrared (NIR) band information (0.45 to 0.72 μm) acquired July 15, 2008.Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site,were overlaid onto the satellite data. SAS 9.1.4® was used to explore univariate statistics and to generate regressionmodels using the field and remote-sampled mosquito and bird data. Regression models indicated that Culexerracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linearprediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2®. Additionally, a model ofthe study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2®.Results: For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogramat a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. Fortotal adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at apartial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the totalbird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinalcount data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km,nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analysesindicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation(R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation(R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationshipbetween total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the totalsampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42.

Conclusion: These data demonstrate that GIS/remote sensing models and spatial statistics can capturespace-varying functional relationships between field-sampled mosquito and bird parameters for determining riskfor EEEV transmission.

IntroductionEastern equine encephalitis virus (EEEV) is the mostdangerous endemic arbovirus in the United States. Upto 70% of symptomatic cases in humans are fatal [1],

and most survivors are permanently debilitated by neu-rologic sequelae [2]. Besides the endemic and economicburdens to humans, frequent equine cases and sporadicmass game bird die-offs are costly consequences ofEEEV transmission [3-5]. Epornitics in wild birds arealso dramatic consequences of EEEV [6], such as die-offs of the endangered whooping crane, Grus americana[7]. Except in Florida [8,9], the ecology of EEEV is less

* Correspondence: [email protected] of Medicine, Department of Infectious Diseases, University ofAlabama at Birmingham, 845 19th Street South, Birmingham Alabama, USA,35294

Jacob et al. International Journal of Health Geographics 2010, 9:12http://www.ij-healthgeographics.com/content/9/1/12

INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS

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understood in the southeastern United States than inother endemic locations in the region. This disease isendemic in Alabama with viral activity varying betweenyears. The summer of 2001 was a particularly activeyear for EEEV, with one human and over 30 veterinarycases in the central and southern regions of the state[10].The mosquito species Culiseta melanura is generally

believed to initiate EEEV transmission to wild birds[11,12]. Passerine birds are the major enzootic reser-voirs, and early transmission among the local avifauna isbelieved to be initiated by ornithophilic species, such asCs. melanura [11-13]. However, peaks in abundance ofCs. melanura species do not correlate directly withpeaks in EEEV transmission [14]. Differences in sampledabundance count data suggest that multiple mosquitospecies are necessary as vectors to account for large epi-zootics [11]. In addition to Cs. melanura, several othermosquito species are likely involved as bridge vectorsfor EEEV transmission. These species include: Aedesvexans, Coquillettidia perturbans, Culex erraticus, whileCulex peccator, Culex territans and Uranotaenia sap-phirina are suspected of circulating EEEV among rep-tiles and amphibians [15,16]. Of these previously listedspecies, it is suspected that Cx. erraticus is the mostimportant EEEV bridge vector between birds and mam-mals in the mid-south, because of frequent virus isola-tions and its abundance in bottomland swamps, floodplains, permanent standing water, recreation areas nearrivers or ponds, and water impoundments in Alabamaand throughout the Tennessee Valley [10,17,18]. Under-standing the spatial distribution of this habitat-restrictedspecies is valuable for predicting risk of EEEV infectionfor nearby human populations.Despite the misnomer “equine,” EEEV transmission

initiates in the avian cycle. Antibody prevalence in wildbirds associated with freshwater swamps in Alabamarange from 6-85% [19], which suggests that differentbird species vary in attractiveness to mosquitoes anddefensive behaviors against mosquito bites [20]. InMacon County, Alabama, avian species overrepresentedin mosquito bloodmeals included: Yellow-CrownedNight-Heron, Carolina Chickadee, Great Blue Heron,Northern Mockingbird, and Wild Turkey [21]. There-fore, determining the spatial distribution of commonbloodmeal hosts of mosquito vectors is a critical step topredicting early cycles of EEEV transmission.Predicting foci of EEEV positive mosquitoes has been

difficult, perhaps as a result of movement of human andhorse populations and fluctuations in bird populationsover the years [9]. Spatio-temporal distribution of arbo-viral vectors and hosts vary over short distances, basedon differences in land cover and meteorological shifts.For example, human cases of West Nile Virus (WNV)

and St. Louis Encephalitis (SLE) clustered in urban/sub-urban areas in Georgia and Alabama [22,23]; whereas,EEEV transmission was restricted to freshwater swampsin Florida [9]. Compared to other arboviral diseases,EEEV transmission tends to be more spatially isolated[8,9], with the notable exception of the 1989 Atlanticand Gulf coast outbreaks, which caused 196 equinecases and 9 human cases [3]. Evidence for spatial isola-tion of EEEV foci include the lack of early warning oftransmission with sentinel flocks and very low serocon-versions of both sentinel flocks (2%) and human popula-tions within EEEV foci (1.7%) [3,8,9,24], suggesting fewasymptomatic cases. Therefore, untargeted or randominterventions would be excessive and wasteful [25], asEEEV vectors and hosts are not randomly distributed.Quantification of vector-host interactions, by incor-

porating high resolution remotely sensed data in GIS,can help predict arbovirus transmission cycles by identi-fying site specific environmental predictors [25-32]. Forexample, in earlier research, Jacob et al. [31] found thatland use land cover (LULC) change sites can aid in spa-tial prediction of human exposure to Culex mosquitoesusing GIS-generated models. A LULC classification,based on Landsat-7 ETM+ data acquired in July 2003and Landsat-5 TM data acquired in July 1991, was com-pared to the abundance of Culex restuans and Culexpipiens egg rafts in Urbana-Champaign, Illinois. TotalLULC change, from 1991 to 2003 in the Urbana-Cham-paign study site, was relatively low (12.1%). The mostfrequent LULC category was maintained urban. Theurban land cover was further subdivided by degree oftree canopy coverage using QuickBird visible and nearinfra-red (NIR) data, which revealed 73.3% of the urbanarea was in the category classified as high canopy cover-age, with 20% of the remotely stratified data categorizedas moderate canopy coverage, and 6.7% as low coverage.The remote stratification of the urban land coverrevealed that 83.3% egg raft distribution was in the highcoverage areas [31].Characteristics of drainage networks and basin physio-

graphic parameters have also been used in hydrologic cal-culations and land cover modeling of flood and swampwater mosquito abundance, using satellite data [32-36].The automated generation of drainage networks hasbecome increasingly popular with the use of GIS andavailability of digital elevation models (DEMs). Thesemodels account for topographic variability and their con-trol over soil moisture heterogeneity and runoff within awatershed by using a flow distance to stream grid-basedanalyses. The advantage of using a flow distance-to-stream algorithm generated in a DEM is that landscapeprofiles can be evaluated and terrain covariates can begenerated, which can estimate relationships between aresponse variable and other environmental-sampled


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variables [35]. Topographic derivatives generated from aDEM can also be calculated at different scales, using thelinear interpolation technique built in GIS, which canaccurately yield several catchment hydrological variables,including percent surface saturation and total surfacerunoff for identification of potential mosquito and aviansampling sites [33].Vector-borne disease risk can also be modeled with

high predictive accuracy by using geostatistical krigingalgorithms in GIS. Kriging is equated with spatial opti-mal linear prediction, where the unknown random-pro-cess mean is estimated with the best linear unbiasedestimator. Kriging field and remote-sampled mosquitoand avian predictor variables require the use of variousgeostatistical techniques to interpolate the parameters ofa random field (e.g., the elevation, z, of the landscape asa function of the geographic location, at an unsampledlocation from data at nearby sampled locations) [34].Stochastic kriging can also be used to generate predic-tion of abundance and distribution data, which canallow for numerical quantification of uncertainty esti-mates in arboviral explanatory covariates [31]. Addition-ally, predicting landscape classes in urban environmentscan reveal local spatial patterns of the physical andsocio-economic factors hypothesized to be associatedwith arboviral transmission. For example, in northernCalifornia, kriging interpolation revealed that Culex tar-salis was the most abundant species in ovitraps nearagricultural sites; whereas, Cx. pipiens was clusteredwithin residential areas [33].The dynamics of transmission of any arthropod-borne

infection is a complex function of many factors, whichmay include the intensity of infection in the vertebratereservoir, the competence of the vector, and the degreeof contact of the vector with the infected vertebrate hostreservoir [37]. Thus, generating models of EEEV, usingfield and remote-sampled mosquito and avian data, isessential to understanding the ecology of EEEV and fordeveloping effective means to control outbreaks. GIS/remote sensing and spatial statistics can map interac-tions between arthropod mosquito vectors and avianamplification host populations, which can aid in spatiallytargeting high density foci of mosquito and avian sam-pling sites [31]. Treatments or habitat perturbationsshould be based on the surveillance of the most produc-tive areas of an ecosystem [25]. Therefore, the objectivesof this research were: a) to generate multiple regressionmodels to determine predictors associated with thesampled mosquito and avian data; (b) to develop spatiallinear prediction models of potential avian and mosquitosampled sites; and, c) to construct a DEM to identifyterrain covariates associated with sampled mosquito andbird data in Tuskegee, Alabama.

Materials and methodsStudy SiteThe study site is located in the Tuskegee National Forestin Macon County, Alabama. Since the site was abandonedin the 1900s, it has undergone extensive re-encroachmentof forest over depleted farmland and is characterized byforested bottomland wetlands [10]. The center of Tuske-gee, AL is located approximately 3 km from the edge ofthe study site, an urban center with a human populationdensity of 3,700 persons/km2 http://factfinder.census.gov.The western edge of the sampling grid abutted the CityLake, east of the center of Tuskegee, an area with a humanpopulation density of 1,100-1,600 persons/km2. The north-west portion of the sampling grid also overlapped withpopulated areas northeast of Tuskegee and north of high-ways US-29/AL-81, with a human population density ofalso 1,100-1,600 persons/km2. The geographic coordinatesof the centroid of the sampling grid were 85.644444 by32.432494 decimal degrees. The central and southern por-tions of the sampling grid had a human population densityof 80 persons/km2, and the eastern edge had a humanpopulation density of 0-50 persons/km2.

CollectionsMosquitoes were collected biweekly from May to Sep-tember 2007, from natural and artificial resting sites, byvacuum collection with a portable backpack aspirator aspreviously described [38]. Briefly, light traps ran fromdusk to dawn and were positioned approximately 2 mabove ground. Vacuum collections were made twice aweek from resting boxes and natural resting sites duringthis same time period. These collections complementedthose from light traps and allowed sampling of mosqui-toes in different physiological/behavioral conditions, i.e.,nulliparous/parous host-seeking mosquitoes in lighttraps versus blood-engorged or gravid ones in restingboxes, or allowed the collection of species not attractedto light. Live material was returned to the laboratory,sorted, identified using a chill table and binocularmicroscope, and frozen at -70°C [39,40]. Point countswere used to estimate bird densities at the study site aspreviously described [21]. Point counts lasted three min-utes, and all birds seen or heard within 100 m of theobserved sites during the three-minute counts wererecorded. Bird counts were conducted using trained,competent observers. Birds were surveyed in a grid of110 points, separated by 250 m within a 1.7 km radiusfrom the center of the study site. The grid points wereselected, systematically based on sampled strategies gen-erated from previous research [21]. Counts lasted threeminutes and were conducted from June 30 through July29, from 0500 until 1100 local time, with all birds seenor heard within 100 m from the observer recorded.


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http://factfinder.census.gov

Remote sensing dataQuickBird data http://www.digitalglobe.com encompass-ing the visible and near infra-red (NIR) bands wasacquired on July 15, 2008 for the study site. QuickBirdmultispectral products provided four discrete non-over-lapping spectral bands covering a range from 0.45 to0.72 μm, with an 11-bit collected information depth.The spatial resolution of the data was 0.61m. The clear-est, cloud-free imagery available of the contiguous sub-areas of the study site was used to identify mosquitoand wild bird sampling sites.

Base mappingBase maps of major roads and hydrological networkswere created using ArcInfo 9.2® (Environmental SystemsResearch Institute, Redlands, California) from differen-tially corrected global positioning system (DGPS) groundcoordinates. In this research, fixed surveillance sites weregeocoded using a CSI-Wireless (DGPS) Max receiverwith a real-time Omni Star L-Band satellite signal, whichhas a positional accuracy of 0.179 m (+/0.392 m) [31]. A10m × 10 m grid-based matrix was overlaid on the basemaps of the study site, in ArcInfo 9.2® to generate effi-cient spatial sampling units. A unique identifier wasplaced in each grid cell. For remote identification of arbo-viral mosquito and avian habitats, the first step is often toconstruct a discrete tessellation of the region [41-48].

Regression analysesA linear regression, with statistical significance, wasdetermined by a 95% confidence level and used to ascer-tain whether the proportions of sampled mosquito datadiffered by grid cell. The linear regression model assumeda random sample between Yi, (sampled mosquito habitatcount data), the regress and regressors Xi1, ... Xip. A dis-turbance term εi, which was a random variable, wasadded to this assumed relationship to capture the influ-ence of all habitat parameters sampled on Yi other thanXi1, ... Xip. The random error term, ε, in a regression ana-lysis of field and remote-sampled Culex aquatic model, istypically assumed to be normally distributed with meanzero and variance s2 [31]. Statistical characteristics of thesampled data were examined in PROC UNIVARIATE.The PLOT option in the PROC UNIVARAITE statementgenerated histograms and boxplots. The NORMALoption was used to test whether the field and remote-sampled parameters had a normal distribution. Theregression analyses was performed using PROC REG.The multiple linear regression model was:

Y X X X i ni i i p ip i= + + + + + = 0 1 1 2 2 1 , , , .

It was important to distinguish the model in terms ofrandom variables and the observed values of the random

variables. Thus, we determined p + 1 parameters b0, ...,bp. In order to estimate the sampled mosquito aquatichabitat parameters, it was useful to use the matrix nota-tion Y = Xb + ε, where Y was a column vector thatincluded the mosquito count values of Y1, ..., Yn, whichincluded the unobserved stochastic components ε1, ..., εnand the matrix X. This matrix was the observed mos-quito aquatic habitat parameter values of the regressorsexpressed as:

X

x x

x x

x x

p

p

n np

=

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

1

1

1

11 1

21 2

1

.

In this research, X included a column that did notvary across the sampled mosquito data, which was usedto represent the intercept term b0.The ecological-sampled data was log-transformed before

analyses to normalize the distribution and minimize stan-dard error. Multicollinearity diagnostics from the COLLINoption in SAS® were estimated. Residual-based diagnosticsfor univariate and multivariate conditional heteroscedasticmodels, previously constructed from clustering field andremote-sampled mosquito habitat parameter estimateshave revealed that errors in variance uncertainty estima-tion can substantially alter numerical predictions modelsdue to multicollinearity [31]. The SAS COLLIN optionproduced eigenvalues and condition index, as well as pro-portions of variances with respect to individual-sampledpredictor variables in the model. The conditional indexscores indicated no significant multicollinearity with themodel. It was hypothesized, however, that serial correla-tion could be a major source of time-varying heterogene-ity. In this research, the Durbin-Watson statistic was usedto detect the presence of autocorrelation in the residualsfrom the regression analysis. The Durbin-Watson can testfor first-order serial correlation [49]. Usually, the Durbin-Watson statistic is used to test the null hypothesis H0:�1 =0 against H1:�1 > 0 [49]. The generalized Durbin-Watsonstatistic is written as:

DWuA A u

u ujj j=′′

ˆ ˆ

ˆ ˆ

where u is a vector of OLS residuals and Aj is a (T - j)× T matrix. In this research, the generalized Durbin-Wat-son statistic DWj was rewritten as:

DWY MA A MY

Y MY

Q A A Qj

j j j j=′ ′

′=

′ ′ ′′

( )1 1

where Q’1Q1 = IT - k, Q’1X = 0, and h = Q’1u.


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http://www.digitalglobe.com

The marginal probability for the Durbin-Watson sta-tistic was:

Pr( ) Pr( )DW j c h< = < 0

where h = h’(Q’1A’jAjQ1 - cI)h.The p-value, or the marginal probability, for the gen-

eralized Durbin-Watson statistic, was computed bynumerical inversion of the characteristic function j(u)of the quadratic form h = h’(Q’1A’jAjQ1 - cI)h. The tra-pezoidal rule approximation to the marginal probabilityPr(h < 0) was:

Pr( )Im[ (( ) )]

( )( ) ( )h

k

kK

k

K

I T< = −+

++ +

=∑0

12

12

12

0

ΔΔE E

where IM[j(·)] was part of the characteristic functionand EI(Δ) and ET(K) were integration and truncationerrors, respectively. The trapezoidal rule is a way to cal-culate the definite integral [49]. A numerically efficientalgorithm was used to quantify the autocorrelated com-ponents in the regression model, which required O(N)operations for evaluation of the characteristic function j(u). The characteristic function was denoted as:

( ) | ( ) | /u iu cj j N kI Q A A Q I− ′ ′ − −−2 1 1

1 2

| | | | | |/ / /V X V X X X− − −′ ′1 2 1 1 2 1 2

where

V I A A and = + − ′ = −( ) .1 2 2 1iuc iu ij j

By applying the Cholesky decomposition to the com-plex matrix V, we obtained the lower triangular matrixG that satisfied V = GG’. Cholesky decomposition is adecomposition of a symmetric, positive-definite matrixinto the product of a lower triangular matrix and itsconjugate transpose [49]. The characteristic functionwas evaluated in O(N) operations by using the followingformula:

( ) | | | * *| | |/ /u = ′ ′− −G X X X X1 1 2 1 2

where X* = G-1X.We tested for serial correlation with lagged dependent

variables in the model (Appendix a). When regressorscontain lagged dependent variables, the Durbin-Watsonstatistic (d1) for the first-order autocorrelation is biasedtoward 2 and has reduced power [50]. If the Durbin-Watson statistic is substantially less than 2, there is evi-dence of positive serial correlation [49]. In AUTOREG,

two alternative statistics (Durbin h and t) can be used totest for time varying residuals that are asymptoticallyequivalent [50]. In this research, we used the h statistic,which was written as:

h N NV= −ˆ / ( ˆ ) 1

whereˆ ˆ ˆ / ˆ = − == ∑∑ V V Vt t tt

N

t

N1

212

, and V was the leastsquares variance estimate for the coefficient of thelagged dependent variable.In PROC AUTOREG, an estimation method was used to

generate an autoregressive error model using the Yule-Walker (YW) method. The YW method can be consideredas generalized least squares using the OLS residuals toestimate the covariances across observation [49]. In thisresearch, we let � represent the vector of autoregressiveparameters, � = (�1, �2,..., �m)’, and we let the variancematrix of the error vector be ν = (ν1, ..., νN)’ be Σ, E(νν’ =Σ = s2V. If the vector of autoregressive parameters � isknown, the matrix V can be computed from the autore-gressive parameters; Σ is then s2V [49]. Given Σ, the effi-cient estimates of regression parameters b were computedusing generalized least squares (GLS). The GLS estimatesthen yielded the unbiased estimate of the variance s2.The YW method alternated estimation of b using gen-

eralized least squares with estimation of �, which theYW equations applied to the sample autocorrelationfunction. The YW method started by forming the OLSestimate of b. Next, � was estimated from the sampleautocorrelation function of the OLS residuals by usingthe YW equations. Then V was estimated from �, andΣ was generated from V and the OLS parameters of s2.The autocorrelation corrected estimates of the regres-sion parameters, b, were then computed using GLS andthe estimated matrix. The YW equations, solved toobtain and a preliminary estimate of s2, were Rj =-r. In this research, we used the equation r = (r1 ..., rm)’,when ri was the lag i sample autocorrelation. The matrixR was the Toeplitz matrix, whose i, jth element wasr|i-j|. Toeplitz matrix is a matrix in which each descend-ing diagonal from left to right is constant [49]. We spe-cified a subset model. Only the rows and columns of Rand r corresponding to the subset of lags specified wereused. The BACKSTEP option was specified for purposesof significance testing. The matrix [Rr] was treated as asum-of-squares-and-cross products matrix arising froma simple regression with N - k observations, where kwas the number of estimated Cx. erraticus habitat para-meters in the model.

Digital elevation modelA three-dimensional model of the study area was con-structed based on DEM statistics generated using


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ArcScene extension of ArcGIS®. The DEM used in thisresearch was a raster representation of a continuous sur-face, originating from the Shuttle Radar TopographyMission (SRTM) which had a spatial resolution of 92 m.The probability distribution of the soil moisture deficit,i.e., statistics of topography, was generated from theDEM data by using a multidirectional flow routing algo-rithm. The purpose of DEM construction was to extracttopographic parameters that may have been associatedwith the field and remote-sampled EEEV mosquito andbird covariates. A flow apportioning algorithm candelineate a realistic channel network for quantifyinghydrogeomorphic properties of simulated drainage pat-terns using DEMs for identifying floodwater mosquitoes[35].

Spatial analysesKriging models were generated using all sampled abun-dance count data in Geostatistical Analyst Extension ofArcGIS 9.2®. However, based on the evidence that Cx.erraticus is likely the primary bridge vector of EEEV inTuskegee [10] and was the most abundant speciessampled in the study site, it was selected for the inde-pendent kriging analyses (Table 1). Also, kriging ana-lyses were run for total abundance counts of thesampled bird data in Tuskegee, in 2007. From

preliminary data analyses, it was determined that North-ern Cardinals were the most abundant avian species inthe study site (Table 2). Therefore, a kriged model wasgenerated using the Northern Cardinal data samplepoints. All the models were created in the ArcGIS 9.3®Geostatistical Analyst Extension.Spatial linear prediction was performed using ordinary

kriging. Geostatistical techniques were used to interpo-late the values Z(x0), at a sampled mosquito or birdhabitat Z(x), for unobserved sampling sites x0 and zi = Z(xi), i = 1... n, using data sampled at nearby sampledhabitat locations (x1,...xn). The kriged-based algorithmcomputed the best linear unbiased estimator, Ž(xo) of Z(x0), for the sampled habitat data, based on a stochasticmodel of the spatial dependence quantified by the vario-gram g(x, y), by expectation μ(x) = E[Z(x)], and by thecovariance function c(x, y) of the random field. In thisresearch, the kriging estimator was given by a linearcombination:

ˆ( ) ( ) ( )Z x w x Z xo i o i

i

n

==∑

1

(2:1)

for analyzing the sampled data; where, zi = Z(xi) wasthe weights while wi (xo) and i = 1... n was the varianceused to minimize any biased condition [35]. The depen-dent variables were the sampled adult count of mosqui-toes or bird data, which were transformed to fulfill thediagnostic normality test prior to performing the kri-ging. The kriging weights were then used to fulfill theunbiasedness condition in the spatial interpolation ofthe ecological-dependent variables using:

ii

n

=∑ =

1

1 (2:2)

Table 1 Adult mosquito counts for the Tuskegee studysite

Mosquito species Adult counts

Cx. erraticus 1,848

An. crucians 808

Cx. territans 632

An. quadrimacluatus 444

Cx. peccator 199

Ae. vexans 193

Cq. perturbans 134

An. punctipennis 126

Ur. sapphirina 124

Cx. quinquefasciatas 82

Cx. restuans 51

Cx. salinarius 45

Cs. melanura 33

Oc. canadensis 26

Oc. spp 14

Cx. nigripalpus 3

Oc. sollicitans 1

Oc. sticticus 1

Oc. triseriatus 1

Or. signifera 1

Ps. columbiae 1

Ps. ferox 1

An. barberi 1

Table 2 Bird counts for the Tuskegee study site

Species Abundance (% of count)

Northern cardinal 119 (37.6)

Carolina wren 77 (20.5)

Red-eyed vireo 51 (8.2)

Indigo bunting 50 (8.0)

Tufted titmouse 36 (5.8)

White-eyed vireo 35 (5.6)

Acadian flycatcher 31 (5.0)

Red-bellied woodpecker 14 (2.3)

American crow 14 (2.3)

Blue jay 13 (2.1)

Carolina chickadee 10 (1.6)

Northern parula 9 (1.4)


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which was given by the ordinary kriging equation sys-tem:

1 1 1 1

1

1

1

n

n

n n n

x x x x

x x x x

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

=

( , ) ( , )

( , ) ( , ))

( , )

( , )

*

*1

1 1 0 1

11

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

−

x x

x xn

The additional parameter μ was a Lagrange multiplierused in the minimization of the kriging error k x2( ) tohonor the unbiased condition in the ecological dataset[51]. The ordinary kriging was given by:

var( ( ) ( ))

( , )

( , )

( , )

* *

*

*

Z x Z x

x x

x x

x x

n

− =

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

1 1 1

1

( , )

( , ) ( , )

( ,x x

x x x x

xn

n n n

1

1

111

1

1 1 0

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

−xx

x xn

*

*

)

( , )

1

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

and the interpolation was given by:

ˆ( *)

( )

( )

Z x

Z x

Z xn n

=⎛

⎝

⎜⎜⎜

⎞

⎠

⎟⎟⎟

′ ⎛

⎝

⎜⎜⎜

⎞

⎠

⎟⎟⎟

1 1

with the error variables quantified using:

var Z x Z x

x x

x xn n

ˆ( *) ( *)

( , *)

( , *)−( ) =

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

′ ⎛

1 1

1

⎝⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟

The semivariogram generated described the spatialdependence, between the sampled mosquito and birdparameters, as a function of the distance between thesampling sites. The semivariogram allowed for mosquitoor bird abundance estimations at any point in the studysite. The value of prevalence, Z, at the coordinate (x0,y0) was estimated from the n nearest sampling values:

Zobs x y Zobs x y Zobs x yn n( , ), ( , ) ( , )1 1 2 2

by the linear formula:

ˆ( , ) ( , )Z x y a Z x yi obs i i

i

n

0 0

1

==∑ (2:3)

The ai were found by the Lagrange multiplier l andsolving the system:

a h h j ni i j i

i

n

( ) ( ), ....., ,+ = ==∑ 0

1

1

under the constraint

aii

n

==∑ 1

1

where hi, j denoted the distance between any two mos-quito or bird sampled locations, located at (xi, yi), and(xj, yj), and hj,0 was the distance between the two mos-quito or bird sampled sites (x0, y0). The semivariancewas defined as g (h) [50-53]. The magnitude of thesemivariance in this research was dependent on the dis-tance between sampled mosquito or bird sites. Semivar-iance of the deviance residuals of the mosquito and birdcount data was calculated, and a variogram was con-structed to determine if there was evidence of latentspatial autocorrelation in the sampled data. The plot ofthe semivariances as a function of distance from a pointis referred to as a semivariogram [53]. The empiricalsemivariogram and covariance can provide informationon autocorrelation components in ecological-sampleddatasets [49].In this research, parameters of a fitted mathematical

function (i.e., the variogram model) included generatinga range, a nugget and a sill. The range is the distance atwhich curve levels of a constant value of semivariancewhich can indicate the spatial scale of a pattern in animage [49]. The range, or active lag distance, is also theapproximate distance at which spatial autocorrelationbetween sampled data point pairs ceases, or becomesmuch more variable [54,55]. The value at which themodel attains the range, (i.e., the value on the y-axis) iscalled the sill, while the nugget is usually assumed to benon-spatial variation due to measurement error and var-iations in the data that relate to shorter ranges than theminimum sampled data spacing [49]. In this research,the sill indicated that the semivariance values had beenreached (i.e., the value of maximum variance wasequivalent to the variance of the image pixel value),while a non-zero intercept value (i.e., nugget variance)of the varigram model was indicative of the variability ofthe field and remote-sampled Cx. erraticus and North-ern Cardinal data quantified at a resolution smaller thanthe image resolution. A simple quantitative measure ofthe interpolation performed was determined by generat-ing root-mean square error (RMSE) values for the mod-els. Optimizing the RMSE by minimizing the spatialstructure in a Culex aquatic habitat model, can generatea pure nugget variogram, of which the level of nugget


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variance can represent noise characteristics in field andremote-sampled explanatory variables [31]. Additionally,a neighborhood distance search radius provided themean standard errors of the interpolated values. Inter-polation accuracy can be measured by the natural loga-rithm of the mean squared interpolation error, whichcan reveal all main effects of parameter estimates, in anautoregressive model, while quantifying several covariateinteraction terms [56-59].

ResultsThe regression models were able to classify sampledhigh and low abundance count habitats. Temperaturehad a significant association with Cx. erraticus adultabundance (p < 0.0002). The predictor variable precipi-tation also presented a significant relationship (p <0.05). In this research, Durbin-Watson statistics weregenerated using the AUTOREG procedure in SAS® to

estimate whether the OLS regression estimates indicatedsignificant serial correlation with an estimated order ofa lagged covariance of 1. The AUTOREG procedurecorrected for serial correlation using the YW method.The Durbin-Watson statistic indicated that serial corre-lation was not significant in the YW corrected model.The YW estimates for the model indicated a R2 = 0.632,F statistics of 39.177, and Durbin-Watson score of 1.935.For total mosquito count data, a first-order trend

ordinary kriging process was fitted to the semivario-gram at a partial sill of 5.041 km, nugget of 6.325 km,lag size of 7.076 km, and range of 31.43 km, using 12lags. For total adult Cx. erracticus count, a first-ordertrend ordinary kriging process was fitted to the semi-variogram at a partial sill of 5.764 km, nugget of 6.114km, lag size of 7.472 km, and range of 32.62 km, using12 lags (Figure 1). For the total bird count data, afirst-order trend ordinary kriging process was fitted to

Figure 1 Predicted Culex erracticus abundance data using ordinary kriged model overlaid on a QuickBird visible and near infra-red(NIR) data of the Tuskegee study site.


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the semivariogram at a partial sill of 4.998 km, nuggetof 5.413 km, lag size of 7.549 km, and range of 35.27km, using 12 lags. For the Northern Cardinal countdata, a first-order trend ordinary kriging process wasfitted to the semivariogram at a partial sill of 6.387km, nugget of 5.935 km, lag size of 8.549 km, and arange of 41.38 km, using 12 lags (Figure 2). To evalu-ate the accuracy of the models, predictive mean stan-dard error distributions were generated, which revealedthat all models were within normal statistical limita-tions (Table 3).

A DEM of the study site was generated in ArcGIS®(Figure 3). The minimum and maximum range of theelevation in the DEM models were calculated. Pearson’scorrelation was used to evaluate the linear relationshipbetween mosquito and bird count data and the sampledpredictor variable elevation using the SRTM DEM.Results of the DEM analyses indicated a statistically sig-nificant inverse linear relationship between totalsampled mosquito data and elevation in meters (m) (R2

= -.426; p < .0001), with a standard deviation (SD) of104.6. The range of the elevation in the DEM had a

Figure 2 Predicted Northern Cardinal abundance count data in the Tuskegee study site using an Ordinary kriging algorithm.

Table 3 Residual model outputs from ordinary kriged models using mean error and root mean square error for thesampled mosquito and bird and count data in the Tuskegee study site.

Data Ordinary kriging mean error Ordinary kriging root mean square error

Total bird counts 0.055 1.821

Northern cardinal 0.163 1,642

Total mosquito counts -0.132 4.664

Cx. erraticus count -4.814 8.535


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minimum value of 0 m, with a maximum value of 431m. The results of the total sampled bird data and eleva-tion were (R2 = -.511; p < .0001), with a SD of 22.97.The range of the elevation in the DEM had a minimumvalue of 0 m, with a maximum value of 439 m. DEMstatistics also indicated a significant inverse linear rela-tionship between total sampled Cx. erracticus data andelevation (R2 = -.471; p < .0001), with a SD = 111.6. Therange of the elevation in the DEM had a minimumvalue of 0 m, with a maximum value of 487 m. Theresults of the total sampled Northern Cardinal data andelevation was (R2 = -.583; p < .0001), with a SD = 114.2.The range of the elevation in the DEM had a minimumvalue of 0 m, with a maximum value of 501 m (Table 4).

DiscussionCulex erraticus was the most abundant mosquito speciescollected during this study in central Alabama bottom-land freshwater wetlands, which was ~6 km from thecenter of Tuskegee and ~1.5 km east of a populatedarea north of highways US-29/AL-81. This species pre-viously yielded the highest number of EEEV-infectedpools in Tuskegee [21]. Habitat requirements of Cx.erraticus are shallow water [60-66], especially overgrownwith surface plants or grassy margins, such as streams,lakes or impoundments [18]. This species may be col-lected in high numbers during hot weather, and evenduring drought, periods in July and August [10]. Blood-feeding hosts of Cx. erraticus include: birds (27-70%),

mammals (23-67%), and reptiles (2-20%), suggestinggreat host flexibility based on relative availability ofhosts [16].The bird communities present in the Tuskegee study

site are typical of reforested areas of bottomland hard-wood [10]. The level of vector contact with differentbird species in a given area is essential in identifying

Figure 3 Digital Elevation Model (DEM) of the Tuskegee study site.

Table 4 Pearson correlation for mosquito and birdsampled data and the sampled predictor variableelevation in the Tuskegee study site.

Predictor variables Statistical tests Significancelevel

Elevation(m)

Total mosquito countdata

PearsonCorrelation

1 -.426

Sig. (2-tailed) <.0001 <.0001

N 141 118

Total bird count data PearsonCorrelation

1 -.511

Sig. (2-tailed) <.0001 <.0001

N 141 118

Cx. erraticus data PearsonCorrelation

1 -.471

Sig. (2-tailed) <.0001 <.0001

N 141 118

Northern cardinal data PearsonCorrelation

1 -.583

Sig. (2-tailed) <.0001 <.0001

N 141 118


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those avian species that are most likely to serve asimportant amplifiers for arboviral enzootics [15]. Birdsthat frequent edge habitats are assumed most likely totransmit EEEV between localized pest mosquitoes andhumans or horses [21]. Woodland and swamp birds spe-cies captured within an EEEV focus in Michigan hadhigh antibody prevalence, but seroconversion of EEEVto other urban bird species in open areas increased dur-ing peak transmission to horses [65].Previous research of EEEV in Alabama also has sug-

gested the importance of hatch-year birds as catalystsfor epidemic transmission. For example, wild birdsinvolved in EEEV transmission in Alabama forestedareas included: Yellow-Crowned Night-Heron, CarolinaChickadee, Northern Mockingbird and Great BlueHeron [21]; in Florida, the species were: Bluejay, North-ern Mockingbird, Rufus-Sided Towhee, LoggerheadShike, Northern Cardinal, Cattle Egret and the game-birds Pheasant and Chukar Partridge [8]; and, in Louisi-ana, the species were: White-Throated Sparrow,Northern Cardinal, House Sparrow, Rufus-SidedTowhee, Carolina Chickadee and Yellow-Rumped War-bler [19]. Similarly, the House Sparrow is a primary vec-tor for WNV, EEEV and SLE in Louisiana [17]. Most ofthese listed birds are included on the Audubon Societychecklist as very common species to Alabama for spring,summer and fall. Notably, birds involved in EEEV tendto be less urban than WNV/SLE avian hosts, thoughconsiderable host species overlap is apparent betweendiseases and between geographic regions [16]. Chicka-dees, Northern Cardinals, Tufted Titmice, Blue Jays,American Crows, Brown-Headed Cowbirds and Red-Billed Woodpeckers inhabit both wooded and human-populated areas [62]. American Crows may forage inwooded locations but roost at night in urban and subur-ban areas [8,46].Culex erraticus was the most common mosquito col-

lected at the study site. Because of its density, it is likelythat Cx. erraticus plays a major role in perpetuatingEEEV transmission. Cx. erraticus is a member of thesubgenus Melanoconion, a largely tropical group of mos-quitoes. Cx. erraticus is the most common member ofthat subgenous in the United States and is distributedthroughout the eastern and the upper midwest andwestward to California [16]. Since Cx. erraticus is a neo-tropical species which plays a major role in the trans-mission of EEEV in Tuskegee, the geographicaldistribution of EEEV in Alabama might resemble thatseen in EEEV foci in tropical areas (e.g., South America).The probability that a mosquito will feed on a reservoirhost is one of the most influential variables affecting thegeographical distribution of an arthropod mosquito vec-tor [8]; therefore, determining spatial patterns of Cx.erraticus host preferences can play a significant role in

controlling the development of avian enzootics of EEEVin the Tuskegee study site.Temperature and precipitation were important predic-

tor variables in the regression model. Environmentalconditions such as temperature, and precipitation, havean important effect on the distribution of the mosqui-toes that harbor arboviruses, thereby, influencing seaso-nal virus activity [3,10,11]. Previous regression analysisestimates derived from multiple field and remote-sampled predictor variables, collected from nine siteswithin Cook County, Illinois, revealed that adult Culexpopulation was positively associated with temperature[57]. The model output indicated that precipitation wasnegatively associated to mosquito abundance in 2002,2003 and 2005 (P <0.05), but positively associated in2004 (P <0.05). Naturally occurring outbreaks of EEEVare usually observed during periods of hot, rainyweather [3]. These weather conditions are ideal forexpansion of Cs. melanura and other mosquito popula-tions [63]. Outbreaks of EEEV in horses and humansare expected to occur from midsummer to late summer,with August being the peak month of incident cases inmuch of the United States [11]. An evaluation of EEEVin horses in Michigan, during five outbreaks betweenyears 1972 and 1991, revealed an increase in Cs. mela-nura, Cq. perturbans, and Ae. vexans as vectors, wildand domestic birds as reservoir hosts, humans and com-mercial poultry flocks as incidental hosts, with anincrease in the state-wide annual precipitation and anincrease in region-specific precipitation [19]. Tempera-ture and precipitation is likely involved in early seasonenzootic transmission and late season epizootic amplifi-cation of the EEEV in wild bird populations at the Tus-kegee study site. It is possible that Cx. erraticus maybecome important as a bridge vector of EEEV in thesoutheastern United States, as human populations con-tinue to move closer to sylvatic sites, where populationsof this mosquito have access to avian reservoirs andduring specific time frames throughout the year, whenthere is fluctuation in meteorological variables. Animportant variable in the amplification from the enzoo-tic cycle of arboviral encephalitides is the degree of con-tact between avian hosts and mosquito vectors [21].Culex erraticus has a minimally infection rate of 3.2,from mid-June to mid-September [16].In this research, there was reasonable overlap between

the kriged predictive maps. Much of this overlap wasrelated to the location of the Tuskegee National Forestponds, which were near the center of the sampling gridand composed of forested land cover. High mosquitoabundance have been found in land cover sites classifiedas having a low-to-moderate range of built environmentand high forest composition [57]. This data suggeststhat birds and mosquitoes frequently forage several


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kilometers beyond their preferred habitats. Mosquitospecies differ in their overall preference for differentclasses of host (e.g., mammals versus birds versus rep-tiles), in the times of day that they are most active inseeking bloodmeals, and the heights at which they for-age [21]. Many of the mosquito hosts sampled wereedge species, birds that are most common at the inter-face between forested and disturbed landscapes, such ascardinals, corvids, and parids. For example, NorthernCardinals were previously found six times more abun-dant in habitats surrounded by open space [11].The semivariograms, generated in ArcGIS Spatial

Analyst, modeled the structure of spatial variability inthe field and remote-sampled Cx. erracticus and North-ern Cardinal habitat data. The semiovariograms wereused to fit models of the spatio-temporal correlation ofthe sampled mosquito and bird parameters. The semi-variogram and covariance functions quantified thenearby habitats as a measure of the strength of statisticalcorrelation and as a function of distance betweensampled habitats. Linear predictors were generated byincorporating models of the covariance of the randomfunction using a weighted moving average interpolation.The semivariogram of residuals from the regressionmodels generated from the sampled covariates, with sta-tionary errors, were used to estimate the covariancestructure of the underlying spatial structural processesin the data.For prediction kriging, the bias of the semivariogram

estimates induced, by using residuals instead of errors,has only a minor effect, as the bias is small for small lags[49]. However, in this research, error in the spatial covar-iance patterns generated from the estimated regressioncoefficients may have been quite substantial due to theexcess land cover heterogeneity between the sampledhabitats. To spatially analyze a mosquito vector, onemust understand that insect populations are typically het-erogeneous in their spatial densities, responding to multi-variate habitat characteristics and environmental controls[25]. Additionally, kriging may not be widely used forpredicting EEEV distribution at a local scale, because ofthe common finding of non-stationarity in ecological-sampled data. The violation of the stationarity assump-tion may affect the validity of kriged surfaces, since acommon metric derived from the semivariogram is notenough to capture spatial variations in EEEV parametersobserved at a local scale. The process of using kriged-based algorithms for mosquito and bird parameters mayrequire estimation of the best model parameters, and anassessment of the resulting model accuracy, before it canbe used as a predictive tool for evaluating EEEV mos-quito and bird habitat data. Diagnostic checking errorresiduals in an EEEV model may enable interventionefforts spatially targeting productive mosquito and bird

habitats based on field and remote-sampled data, byusing the asymptotic distribution of parameter estimatesfrom a residual autocovariance matrix.The distribution of Northern Cardinals was interest-

ing, as this species was by far the most abundant, preva-lent, and evenly distributed bird species detected in theTuskegee study site. This species has also expandedrange over the last century due to mild winter patternsand increased use of bird feeders for winter survival[11]. Despite the very high abundance of Northern Car-dinals, this species appeared only moderately attractiveto host-seeking mosquitoes [10]. Northern Cardinals arecommon in both forest and urban habitats [19] Radiotracking data suggests that individual cardinals arehighly territorial of small plots of land and do not typi-cally migrate more than 50 m from their roosts [14]. Itis possible that the high abundance and prevalence ofNorthern Cardinals prevented smaller passerine EEEVhosts from reaching higher densities. The spatial pat-terns of total bird counts, Northern Cardinal counts, didnot significantly differ; whereas, the spatial pattern ofCx. erraticus significantly differed from the overall mos-quito counts.Broad-scale quantification of topography, using the

spatial hydrological model of the study site, visually dis-criminated specific land cover features associated withthe mosquito and bird-sampled data with good accu-racy. The DEM captured all hydrologic characteristics,determining the flow paths of streams, e.g., watershedboundaries in the study site. Elevation was found to besignificantly associated with the sampled mosquito andbird data in the study site. Elevation is directly relatedto temperature, which effects mosquito survivorship[35]. Because many birds rest in trees, it can be arguedthat mosquitoes will search for bloodmeals in trees,where their avian hosts would be found. Therefore,mosquito preference for bird hosts influences theirattraction to higher elevation. Frequent blood feedingcan occur by Culex mosquitoes on abundant urban pas-serine birds [35]. Feeding, primarily on EEEV-competentavian hosts during the amplification period of the epide-miological cycle, maximizes the intensity of the epi-demic in mosquitoes [64].Modeling field and remote-sampled explanatory vari-

ables of EEEV in newer GIS offer an attractive and bet-ter alternative to traditional disease mapping approach.Such an approach lends itself to the development ofpowerful predictor models for estimating multiple envir-onmental-sampled variables of EEEV mosquito andavian sampling sites. Newer GIS software packages pro-vide a wide range of tools for data analysis, using carto-graphic modeling for identifying mosquito and bird dataand estimating spatial dependency in the sampled ecolo-gical datasets. For example, a GIS-based model can


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generate sampling prediction error distributions that arewell defined for identifying explanatory variables asso-ciated with prolific mosquito and avian habitats basedon sampled count data. Therefore, GIS/remote sensingmaps of mosquito and bird data associated with EEEVhave a direct use in public health programs and targetedinterventions. Monthly risk maps, showing the relativedanger of regional EEEV transmission, based on mos-quito and bird abundance data, can be constructed. Riskmaps can then be updated weekly as epidemic triggersare identified and quantified, enabling GIS/remote sen-sing-based spatial predictions of favorable future condi-tions for EEEV transmission [25].Additionally, a graduated, systematic GIS sampling

methodology can adjust for sampled ecological covari-ates, a technique that can identify more georeferencedCx. erraticus and wild birds habitat clustering siteswithin urban environments than random sampling stra-tegies. A major advantage of using GIS-based models isthat the sampling prediction error distributions are welldefined for identifying explanatory variables associatedwith prolific mosquito and avian habitats based onsampled count data. Therefore, designing and develop-ing control strategies, based on GIS/remote sensingdata, and models can provide effective entomologicaltools to reduce mosquito arboviral vectors in conjunc-tion with high density foci by identifying critical featuresof landscape for locating productive areas in a studysite. Since it is more feasible to expand surveys to tar-geted habitats, based on spatially selected potential foci[31], a systematic GIS surveillance sampling frame,using QuickBird data and geostatistical predictive algo-rithms, can focus on specific habitats, which can allowfor intensified entomological surveillance at specifichabitats, while not increasing overall sampling efforts.Random interventions are excessive and wasteful, asarboviral vectors are not themselves randomly distribu-ted [25] and spatio-temporal sampled abundance countsof mosquito and bird habitats fluctuate constantly [37].Substantive variations in mosquito and bird abun-

dance, in relation to arboviral infections, pose a chal-lenge for surveillance programs; yet, spatial statistics andGIS surveillance sampling strategies have not beenapplied in correspondence to these changes in Alabama.For example, in 2007, 24 human cases of WNV werereported to CDC ArboNET, 13 of which were in Mon-tgomery County; however, mosquito sampling has beenlimited to only the north quadrant of Alabama in recentyears [67]. Bird data also were collected for every countyin Alabama in 2002-2003 [68]; however, reporting hassince stopped in most counties. Although an epidemicof SLE cases in humans occurred in 1975 in Birming-ham, Alabama (32 cases) [23], which led to formation ofa SLE mosquito surveillance program, this program

no longer exists and should be replaced. The wide geo-graphical range of the 1975 epidemic [69] highlights cri-tical need for interdisciplinary surveillance in Alabamaand other southeastern states. Similar epidemics inArkansas (1991) and Louisiana (2002) revealed Culexquinquefasciatus as the primary vector of SLE and/orWNV [70]. The high seroprevalence (36%) of primatesexposed outdoors during the 2002 WNV epidemic inLouisiana suggested that human exposure risks are alsolikely high[17]. Therefore, endemic pathogens for EEEV,WNV and SLE can be target systems used to developsurveillance models that incorporate predictive algo-rithms; as well as field-sampled and remotely senseddata.The surveillance system described in this paper could

also be incorporated to develop strategies for the detec-tion of avian influenza. The early warning signs (i.e.interaction of vector-host-virus) suggest a valid concernthat Alabama is unprepared in the event of an avian flupandemic. Low pathogenicity H5N1 strains have beendetected in wild bird migratory populations in the Uni-ted States [67]. Potential routes for introduction of theH5N1 virus into Alabama include migration of infectedwild birds. Recent trends suggest that H1N1 can nowremain virulent in ducks for longer durations, whichmay allow water fowl to shed the virus as they migratethrough areas of outbreaks [71]. Reports of 87 positivesfor H1N1 from carcasses of 19 avian species in Swedenand Denmark suggest that avian influenza is on themove [72].Furthermore, replication of the GIS sampling techni-

ques, and the statistical algorithms used in this research,can provide detailed distributions for targeting highlyproductive mosquito habitats in other geographic areasoutside of the United States. For example, the modelsgenerated in this research can be used to control otherencephalitis-type, enzootic arboviral diseases, such asVenezuelan equine encephalomyelitis virus (VEE), whichis transmitted by mosquitoes endemic in Central Ameri-can and South American countries [73]. VEE is causedby encephalitic alphaviruses in the family Togaviridaesimilar to EEEV. Encephalitic alphaviruses have causedrepeated epidemics and equine epizootics since the1920s. For example, a major outbreak of VEE in Vene-zuela and Colombia, during 1995, involved an estimated100,000 people [73]. Therefore, quantifying enzootic fociof VEE surveyed in unknown sites in a particular geo-graphic region (e.g., Mexico), using multi-temporalsampled QuickBird data in GIS, coupled with groundsurveillance data, can be essential for designing patho-genesis studies, simulating natural infection of verte-brates. Characterization of various statistical techniquescan then be applied to the sampled data to identify highdensity habitats in determined areas, where


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environmental-sampled predictors may not be the onlyfactor influencing productivity. Post-classification caninclude validating VEE mosquito parameters usingextensive ground truthing to identify regions of highlyprolific habitats.In conclusion, regression models revealed that tem-

perature and precipitation had significant associationwith sampled Cx. erraticus adult abundance count data.Kriging techniques developed a spatial linear predictionmodel of potential mosquito and bird habitats for theTuskegee study site. The application of kriging reducedconstraint on the interpolated value of the sampledmosquito and bird abundance data, to take advantage ofdistance and direction in the interpolation process andto minimize the variance of unexpected error. Topo-graphic descriptors, derived from the DEM, supported aquantitative analysis of the spatial distribution and con-figuration of the georeferenced mosquito and birdsampled data in the study site. Mosquito indicatorscombined with other environmental information such astemperature and precipitation, wild bird population, andEEEV strains, may offer more precise evaluation ofhuman EEEV disease risks. Continued development ofspatio-temporal models, using GIS, remote sensing dataand spatial statistics, can estimate vector and infectedhost distribution, which can further predict the distribu-tion of EEEV based on ecological-sampled covariates.

Appendix AUsing the AUTOREG procedure for generating theGeneralized Durbin-Watson Tests from the ecologicalsampled EEEV parametersInitially we used the regression model: Y = Xb + νwhere X was an N × k data matrix, b was a k × I coeffi-cient vector, and ν was a N × I disturbance vector. Theerror term ν was assumed to be generated by the jth-order autoregressive process νI = εI - �jνI - j where |�j| <I, εI was a sequence of independent normal error terms,generated from the analyses of the EEEV data withmean 0 and variance s2. We used the Durbin-Watsonstatistic to test the null hypothesis H0 : �1 = 0 againstH1 : -�1 > 0. The generalized Durbin-Watson statisticwas:

dVt Vt jt j

N

VttNj =

− −= +∑

=∑

( )21

21

where V were OLS residuals. We used the matrix

notation, d jj j=

′ ′′

Y MA A MY

Y MY

where M = IN - X(X’X)-1X’ and Aj

was a (N - j) × N matrix:

A j =

−−

−

⎡

⎣

⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥

1 0 0 1 0 0

0 1 0 0 1 0

0 0 1 0 0 1

and there were j - I zeros between -I and 1 in eachrow of matrix Aj. The QR factorization of the designmatrix yielded a N × N orthogonal matrix Q: X = QRwhere R was an N × k upper triangular matrix. Thereexisted an N × (N - k) sub-matrix of Q such that Q1Q’1 =M and Q’1Q1 = IN - k. Consequently, the generalized Dur-bin-Watson statistic was stated as a ratio of two quadratic

forms: d jjl ll

n

lln

= =∑

=∑

21

21

where lj1... ljn were the upper n

eigenvalues of MA’jAjM and ξI was a standard normalvariate, and n = min(N - k, N - j). These eigenvalues wereobtained by a singular value decomposition of Q’1A’j. Themarginal probability (or p-value) for dj given co was

Prob Prob( ) ( )

jl lln

lln

c q j

21

21

0 0=∑

=∑< = <

where q cj jl ll

n= −

=∑( ) 0

2

1.

When the null hypothesis Ho : �j = 0 held, the quad-ratic form qj had the characteristic function

j jll

nt c it( ) ( ( ) ) /= − − −

=∏ 1 2 0

1 2

1

The distribution function was uniquelydetermined by this characteristic function:

F xeitx j t e itx

j t

itdt( )

( ) ( )= +

− − −∞

∫12

22 0

We tested Ho : �4 = 0 given �1 = �2 = �3 = 0 againstH1 : -�4 > 0, using the marginal probability (p-value)

and : F dtt tit( ) ( ( ) ( ))0 1

21

24 4

0= + − −∞

∫

where 4 4 41 2

11 2( ) ( ( ) ) /t d itl

l

n= − − −

=∏ and d4 was

the calculated value of the fourth-order Durbin-Watsonstatistic from the ecological sampled EEEV data.

Author details1School of Medicine, Department of Infectious Diseases, University ofAlabama at Birmingham, 845 19th Street South, Birmingham Alabama, USA,35294. 2Department of Entomology and Plant Pathology, Auburn University,301 Funchess Hall, Auburn, Alabama, USA 36849. 3NASA -NSSTC, GlobalHydrology and Climate Center, 320 Sparkman Drive, Huntsville, Alabama,USA 35805. 4Department of Anthropology, University of Alabama atBirmingham, 1401 University BLVD, Heritage Hall Room 360, Birmingham,Alabama, USA. 5Department of Biological Sciences, Auburn University, Room


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101, Rouse Life Science Building, Auburn, Alabama USA, 36849. 6Departmentof Entomology and Plant Pathology, Auburn University, 301 Funchess Hall,Auburn, Alabama, USA 36849. 7Global Infectious Disease Research Program,Department of Public Health, College of Public Health, University of SouthFlorida, 3720 Spectrum Blvd, Suite 304, Tampa, Florida, USA 33612.

Authors’ contributionsBJ conceived the study and led the drafting of this manuscript; JL and SPcontributed to the interpretation and results of the remote models. NBsupervised the mosquito data collection; GH supervised the bird datacollection; CM, LE, EC and RN helped in the entomological analyses of themanuscript; TU is the principal investigator of the study. All authorsinterpreted the results and wrote the paper.

Competing interestsThe authors declare that they have no competing interests.

Received: 21 April 2009Accepted: 24 February 2010 Published: 24 February 2010

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doi:10.1186/1476-072X-9-12Cite this article as: Jacob et al.: Developing GIS-based eastern equineencephalitis vector-host models in Tuskegee, Alabama. InternationalJournal of Health Geographics 2010 9:12.

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http://wayne-health.org/pdf/arboguide-surveillance.pdf

http://wayne-health.org/pdf/arboguide-surveillance.pdf









http://www.cdc.gov/ncidod/dvbid/westnile/

http://www.cdc.gov/ncidod/dvbid/westnile/

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http://www.cdc.gov/ncidod/dvbid/westnile/birdspecies.htm









Developing GIS-based eastern equine - BioMed Central

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