Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama

Jacob, Benjamin; Burkett‐Cadena, Nathan D.; Luvall, Jeffrey C.; Parcak, Sarah; McClure, Christopher J. W.; Estep, Laura K.; Hill, Geoffrey E.; Cupp, Eddie W.; Novak, Robert J.; Unnasch, Thomas R.

doi:10.1186/1476-072x-9-12

Cited by 21 publications

(39 citation statements)

References 68 publications

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“…LIS has the ability to run several "tiles" within a sub-meter resolution digitized grid cell that has different land use classifications (www.nasa.gov) so even if a digitized cell classified at the Hillsborough study sites was for example 5% urban, that portion could still be remotelyhmonitored. For example, in previous research Jacob et al [19] generated an LIS map using field and QuickBirdgeosampled explanatory predictors of Culex. pipiens/restuans for 15 larval habitats in Urbana/Champaign, Illinois USA.…”

Section: Discussionmentioning

confidence: 99%

“…The conversion from radiometrically corrected image pixels to spectral radiance uses the following general equation for each band of a WorldView-3 product: Citation count -1 ) for a given band, q Pixel,Band are radiometrically corrected image pixels (counts), and ∆λ Band is the effective bandwidth (µm) for a given band. Conversion to TOA spectral radiance involved two major steps: multiplying radiometrically corrected image pixels by the appropriate absolute radiometric calibration factor (K) to obtain a band-integrated radiance (Wm -2 sr -1 ) and then dividing the result by the appropriate effective bandwidth to get spectral radiance (Wm -2 sr -1 µm -1 ) [19].…”

Section: Gismentioning

confidence: 99%

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A Negative Binomial with a Non-Homogenous Gamma Distributed Mean for Robustifying Pseudo R2 Regression Values of Immature Vector and Nuisance Mosquito Count Data for Optimally Discerning Un-Geosampled Waste Tires in a Subtropical Oviposition Site in SAS®/GIS Utsing Worldview-3 Visble and Near Infra-Red Data in Hillsborough County, Florida

Etn¹,

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2016

J Remote Sensing & GIS

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Refuse vehicle tires on undeveloped land plots near human dwellings may be a public health threat, as they can provide a suitable habitat for vector and nuisance mosquito (Diptera: Culicidae) population growth. These tires are currently found only through ground-based searches, so interpolated spectral signature of a geo-referenceable, known, positive tire may help expedite discriminating unknown waste tire geolocations. However, frequentistic and nonfrequentistic quantification of bioenvironmental explanatorial time series covariates statistically significant to mosquito hyperproductivitt in waste tire habitats is needed to limit the search criteria of the signature. This study aimed to develop an iteratively interpolative geo-spectral biosignature for detecting unknown, un-geosampled waste tires conducive to mosquito propagation. After constructing various regression models, we found that the field geo-sampled mosquito count data featured deviations from the assumptions of regression modeling (i.e., collinear and heteroskedastic parameters). Thus, a negative binomial paradigm was utilized to assuage the violations of regression analysis and to robustify the model's R 2 value. Based on the results of the linear analyses, a spectral signature of a productive habitat was created from multispectral band imagery from WorldView-3 satellite sensor data. The signature was then applied in Hillsborough County, FL to remotely determine the eco-geographical geo-locations of anthropogenic waste tire habitats. The signature model exhibited a sensitivity of 83% and a specificity of 87%. In conclusion, the regression and signature models constructed here provided a parsimonious yet accurate estimation of undiscovered waste tire habitats that may yield many mosquitoes. [5] composed geo-optical algorithms for decomposing sub-meter spatial resolution (i.e., panchromatic Quickbird 0.61m IFOV data) imagery of rice field environments in order to geo-locate undiscovered productive aquatic larval habitats of malaria mosquito vector of Anopheles arabiensis (Diptera: Culicidae). The reference biosignatures of these habitats generated from the unmixing algorithmic geomteri-optical models were then used to perform an ordinary krig-based interpolation in ArcGIS ® [5]. Likewise, Jacob et al. [6] geo-predicted seasonal trailing vegation, discontinuous, infrequently canopied, turbid water, seasonal black -fly vector of onchocercisasis, Simulium damnosum s. l. (Diptera: Simuliidae) by extracting spectral end members of canopy shaded riverine sites featuring black Precambrian rock from QuickBird imagery [6]. The end members were decomposed to orthogonal eigenevctors render a specified graphical

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Section: Discussionmentioning

confidence: 99%

Section: Gismentioning

confidence: 99%

A Negative Binomial with a Non-Homogenous Gamma Distributed Mean for Robustifying Pseudo R2 Regression Values of Immature Vector and Nuisance Mosquito Count Data for Optimally Discerning Un-Geosampled Waste Tires in a Subtropical Oviposition Site in SAS®/GIS Utsing Worldview-3 Visble and Near Infra-Red Data in Hillsborough County, Florida

Etn¹,

Bg²

2016

J Remote Sensing & GIS

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“…quinquefasciatus habitats such as manual profiling from photogrammetric stereo models, digitizing of contours, digitizing topographic map contour plates, converting hypsographic and hydrographic tagged vector files and also for performing autocorrelation via automated photogrammetric systems. To obtain DEMs from satellite images for determining vector amplification and host population dynamics for mapping the spatiotemporal distribution of WNV from high resolution data two methods are possible: along-track stereoscopy from the same orbit using fore and aft images and across-track stereoscopy from two adjacent orbits [16]. The four possibilities of stereo image processing for DEM generation are Fore-Aft, Fore-Nadir, Aft-Nadir and Fore-Aft-Nadir images as a pair [19].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Jacob et al (2010) used multispectral QuickBird data and DEM-based GIS methods to evaluate stream flow direction for determining satellite-derived characteristics of drainage basin physiographic predictors associated with Culex erraticus, a mosquito vector of eastern equine encephalitis virus (EEEV) and Northern Cardinals, an avian host associated with WNV and EEEV in Tuskegee, Alabama [16]. Eastern equine encephalitis virus is a virulent arbovirus maintained in an enzootic cycle between local avian fauna and ornithophilic mosquitoes in coastal regions of the eastern United States [1,14,16]. Euclidian distanceto-nearest hydrological body was calculated as the distance in geo-space from a grid cell to a stream grid cell defined by a Stream Raster Grid.…”

Section: Introductionmentioning

confidence: 99%

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Adjusting Second Moment Bias in Eigenspace Using Bayesian Empirical Estimators, Dirichlet Tessellations and Worldview I Data for Predicting Culex quinquefasciatus Habitats in Trinidad

Jacob¹,

Chadee²,

Novak³

2011

JGIS

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Temporally weighted regression models with a spatial autoregressive component may estimate nonlinearities in spatiotemporal-sampled data of Culex quinquefasciatus, a major vector of West Nile Virus (WNV) which can help implement control strategies by determining optimal predictors associated to prolific habitats. The design of this kind of mixed model can specifically incorporate spatial autocorrelation whilst including the influence of other aspatial predictor variables. Currently, the lack of an estimation theory that allows for heteroscedasticity and corresponding joint hypothesis testing in the presence of spatial dependence in georeferenced Cx. quinquefasciatus habitat data is a serious shortcoming in WNV research. In this paper we used spatially lagged and simultaneous autoregressive models based on multiple predictor variables of immature Cx. quinquefasciatus and Worldview 1 (WV-1) data to help implant a remote habitat-based surveillance system in Trinidad. Initially, we used Geomatica Ortho Engine ® v. 10.2 for extracting a Digital Elevation Model (DEM) from the WV-1 raw imagery. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled Cx. quinquefasciatus data and elevation (m) (R 2 = −0.439; p < 0.0001), with a standard deviation of 10.41. Additional field-sampled information was derived using data from an orthogonal grid-matrix constructed in an ArcInfo 9.3 ® and overlaid onto the WV-1 data. A unique identifier was placed in the centroid of each grid cell. Univariate statistics and Poisson regression models were then generated using the georeferenced covariates in SAS/GIS ® . Coefficient estimates were also used to define expectations for prior distributions in a Bayesian estimation matrix using Markov Chain Monte Carlo (MCMC) specifications. A spatial residual trend analyses was then performed using autocorrelation indices which linked tabular data in SAS PROCLMIXED ® with the egg-raft count data in ArcInfo ® . The estimation matrix identified prolific habitats based on the covariate distance to the nearest house. An Ordinary kriged-based interpolator was then constructed in Geostatistical Analyst Extension of ArcGIS 9.3 ® based on the adjusted Bayesian estimates. For total Cx. quinquefasciatus egg-raft count, first order trend was fitted to the semivariogram at a partial sill of 5.931 km, nugget of 6.374 km, lag size of 7.184 km, and a range of 31.02 km using 12 lags. We assessed the performance accuracy of the interpolation procedures based on the magnitude and distribution of errors between observed and model-predicted values using Voroni tessellations. These residuals divided the space between the individual georeferenced Cx. quinquefasciatus habitats by XY coordinates in 2-dimenisional space which revealed that the geophysical parameter error residuals in the interpolation model were within normal statistical limitations. Newer GIS software and WV-1 data can generate highly accurate predictive Cx. quinquefasciatus habitat distrib...

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Bats as Potential Reservoir Hosts for Vector-Borne Diseases

Melaun

Werblow

Busch

et al. 2013

Bats (Chiroptera) as Vectors of Diseases and Parasites

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Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama

Cited by 21 publications

References 68 publications

Adjusting Second Moment Bias in Eigenspace Using Bayesian Empirical Estimators, Dirichlet Tessellations and Worldview I Data for Predicting Culex quinquefasciatus Habitats in Trinidad

Bats as Potential Reservoir Hosts for Vector-Borne Diseases

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