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Int. J. Risk Assessment and Management, Vol. 6, Nos. 4/5/6, 2006

Exploring GIS, spatial statistics and remote sensing for risk assessment of vector-borne diseases: a West Nile virus example Thomas R. Allen* Department of Political Science and Geography, Old Dominion University, Norfolk, VA 23529-0088, USA E-mail: [email protected] *Corresponding author

David W. Wong Earth Systems and GeoInformation Sciences, School of Computational Sciences and Geography, George Mason University, Fairfax, VA 22030, USA E-mail: [email protected] Abstract: Vector-borne diseases pose a continuing health risk to the global population. In the United States, the spread of West Niles virus (WNV) in the past several years across the country has been alarming. Using WNV as an example, this paper demonstrates how geographic information science (GIScience) and attendant technologies in remote sensing, geographic information systems (GIS), and spatial statistics, can be used for the surveillance and control of disease vectors in general, and specifically mosquitoes in the case of WNV. Data supporting the analyses consist of local field surveillance data, population demographic data, and remote sensing data for habitat characterisation and environmental conditions affecting mosquito vector breeding. The results of this paper provide evidence that these technologies, integrated in scientific methods, provide valuable information to formulate risk management policies and actions. Keywords: geostatistics; mosquitoes; remote sensing; West Nile virus. Reference to this paper should be made as follows: Allen, T.R. and Wong, D.W. (2006) ‘Exploring GIS, spatial statistics and remote sensing for risk assessment of vector-borne diseases: a West Nile virus example’, Int. J. Risk Assessment and Management, Vol. 6, Nos. 4/5/6, pp.253–275. Biographical notes: Thomas Allen, PhD in geography, has research interests in remote sensing, geographic information systems (GIS) and environmental change. His current work analyses satellite-based change detection, coastal environments, and landscape epidemiology. David Wong, PhD in geography, has research interests in population analysis, spatial scale issues and the use of GIS for spatial statistical analysis. His recent projects include environmental assessment and developing procedures in GIS for population analysis.

Copyright © 2006 Inderscience Enterprises Ltd.

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T.R. Allen and D.W. Wong

1 Introduction The year 2003 marks an unprecedented time in the history of environmental epidemiology and public health. Even though the spread of infectious diseases and pathogens from animals to humans has been extensive historically and geographically, such as the relatively geographically confined Ebola and hantavirus, and the more regional-wide diseases such as the Lyme disease and West Nile virus, the rapid outbreak of Severe Acute Respiratory Syndrome (SARS) in Asia and then Canada and beyond has created almost a true pandemic (world-wide human epidemic). While it was expected that the speed of spreading these animal-originated diseases to humans is not going to slow down (Working Group in Emerging and Re-emerging Infectious Diseases, 1995), the risk to human health is increasing everyday, according to the Centers for Disease Control and Prevention (Center for Disease Control and Prevention, 2003; Nash et al., 2001a). Although advances in medical technology related to infectious diseases will offer cures when such diseases occur, there is need for a more proactive approach and preventive measures to minimise the outbreaks of these diseases or to monitor the spread of these diseases when they occur. Since many of these diseases have an environmental or geographical dimension (e.g. (Hales et al., 1999; Walker et al., 1996)), it is logical to use Geographic Information Systems (GIS) technology to manage and monitor different aspects of these diseases, from simple tracking, to epidemiologic analysis, assessment of human health risks, or assuring access to health services (Cromley and McLafferty, 2002). Further, many of these diseases are regarded as vector-borne diseases dispersed by vectors such as mosquitoes, ticks, or other intermediate organisms, in some cases the pathogen residing in a reservoir host (e.g. birds.) In such cases, GIS can be useful to analyse and monitor the spread of the diseases if the vectors have explicit and observable spatial behaviours or patterns linked to environmental factors, such as climate change. In this paper we focus on vector-borne diseases. Infectious diseases are caused by agents or pathogens, which are microorganisms. They can be spread to humans or animals, which are regarded as hosts, either directly or through a vector. The vector is usually an insect, such as ticks in Lyme disease or Rocky Mountain Spotted Fever, or mosquitoes in West Nile virus or Eastern Equine and other forms of encephalitis. Because these vectors are housed in areas with certain environmental characteristics and humans have settled or co-existed with these vectors, GIS and remote sensing technologies can assist in identification of the environments favourable to the formation of the vector reservoirs as well as assess the risk of human exposure to these vectors (i.e. risk of disease transmission). In this paper, we demonstrate how GIS in combination with other geographical information technologies (Goodchild, 1992), namely remote sensing and spatial statistics, can be used to analyse and manage vector-borne diseases. We specifically use West Nile virus as an example to illustrate the use of GIS and related technologies to identify areas favourable to the breeding of the mosquito vectors and to assess the risk of this disease to a local population. This paper will focus on the Northern Virginia area, southwest of Washington, D.C. This area is chosen because of the abundance of data related to West Nile virus, an historically large and growing human population, relative logistical ease of data collection in the urban environment, and proximity to collaborating personnel in local government and the private environmental health sector. The proposed method and the assessment results can provide guidance to public officials for managing the

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environmental conditions that may affect the disease reservoirs, to identify appropriate environmental health control measures, and to formulate public health policy. We hope that our methods and approaches can offer some insights into developing economically efficient, effective, but also environmentally friendly control methods, which may also be applicable to the management of other vector-borne diseases.

2 Background West Nile virus (WNV) has captured the media and research community’s attention far beyond any recently emergent vector-borne disease in decades in the United States. WNV appeared in the US in 1999, but historically emerged in a 1937 outbreak in Uganda. Outbreaks have since occurred sporadically in Africa, Asia, Eastern Europe and now North America (Goodyer, 2002). The principal cycle of transmission for WNV is from birds to mosquitoes to humans. This cycle terminates with illness or death in an infected human; however, the virus is believed to over-winter in mosquito populations in the southeast and to reside indefinitely within the bird ‘reservoir’ population. Commonly known to bite both humans and birds, Culex species mosquitoes typically breed underground in the foul water standing in urban drains and catch basins. These catch basins, storm water drains, and retention ponds, are critical areas for surveillance and larviciding in urban mosquito control. By virtue of their enclosed nature, such basins may require field surveillance or mapping by the local municipality, and most urban centres have such infrastructure within their geographic information system (GIS) databases. Mosquitoes can retransmit and ‘amplify’ viruses in local bird populations. During large mosquito ‘bloom’ events, a rich reservoir, abundant vectors, and humans in proximity may lead to extensive epidemic or epizootic (disease in birds and animals) events. Prevention of human cases of WNV usually involves

• •

Avoidance of bites by using sprays or lotions containing DEET Eradication of vector species in their breeding areas (larviciding) or hunting stages (adulticiding by spraying) or by encouragement of biological predators (e.g., bats, purple martens, etc).

Vector control agencies and public cooperation are needed to monitor ponds and standing water and to drain or remove water bodies to limit breeding. Our study will use several techniques under the umbrella of geographic information technology. These techniques include remote sensing, geostatistics and geographic information systems (GIS). GIS are mainly used to store the spatial data which provide the location information of events, features and characteristics of the spatial processes of phenomena. The system offers an environment in which spatial analysis and geostatistical modelling can be performed. One particular strength of GIS is their capabilities in displaying spatial data and the results of analysis in maps. In the remaining part of this background section, we will briefly describe the spatial analytical tools, the role of remote sensing, the study area, and the data supporting the analyses.

2.1 Point pattern analysis: kernel density estimation In studying vector-borne diseases in general, and WNV specifically, data often appear as points or discrete events. This study relies heavily on analysing these point data in terms

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of their spatial patterns using geostatistical techniques. Kernel density estimation (KDE) is one of these techniques used to create a surface to indicate the intensity of the events or the phenomenon. After Bailey and Gatrell (1995), assuming that s1 . . . sn are locations of the events, then (s), the intensity of the event can be estimated by n 1  ( s − si )  λˆτ ( s) = ∑ 2 k  , τ  τ  i

(1)

where is the bandwidth or the size of the kernel and k is the kernel function determining the shape of the kernel. While the kernel function is a bivariate probably density function, which may take different forms (e.g. the most common form being the quartic kernel), different specifications of the function can provide equally reasonably results (Bailey and Gatrell, 1995). However, the size of the kernel, or the bandwidth, may have more significant impact on the results. In general, using a smaller bandwidth will limit the density estimation to the local situation. That is, the intensity estimated for location s will be limited to the events or density in the immediate neighbour of s if a relatively small bandwidth is used. As a result, spatially more discrete patterns or surfaces are generated. On the contrary, using a large bandwidth will incorporate information from far away in the estimation process, yielding smoother surfaces of the derived intensity. Generating the intensity surface of a given event is quite useful for exploratory analysis, not just in disease mapping (Gatrell, 2002). Several application examples of KDE for crime analysis can be found in chapter eight of the CrimeStat manual (http:// www.icpsr.umich.edu/nacjd/crimestat.html). Often, the density surface identifies or exposes the so-called ‘hot spots’ when events are spatially clustered to a local area, or ‘cold spots’, where the events are much less frequent in the area. We will apply the KDE technique in an exploratory analysis of WNV based upon the locations of WNV-positive dead birds. The purpose is to identify ‘hot spots’ indicating the spatial clusters of the presence of WNV, though these clusters do not necessarily represent the actual transmission site of the disease.

2.2 Geostatistics and remote sensing Comparison of WNV to other outbreaks immediately draws one to the need to understand ‘Landscape Epidemiology,’ described by Meade and Earickson (2001) as, ‘. . . geographic delimitation of the territory of a transmitted disease in order to identify cultural pathways for disease control.’ Among the tools of landscape epidemiology, field mapping and regionalisation are used to inventory spatial locations and characteristics of habitats in conjunction with clinical epidemic surveillance. Mapping precise locations and conditions of disease transmission is extremely significant evidence for breaking the transmission paths and protecting the population (Meade and Earickson, 2001). Remote sensing, coupled with the growing capacity of local-level vector control programmes’ GIS and GPS infrastructure, would provide the needed technological means to perform their critical functions more efficiently and effectively. Early warning systems, as an example, may combine GIS and remote sensing and include reporting systems, risk mapping, and environmental early warning systems (Myers et al., 2000). In this project, field mapping is done using standard entomological and vector health techniques (light traps, gravid traps, and surveillance for dead birds and human disease transmission).

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These tools are augmented by the technological innovations of rapid viral assay (e.g. ‘Vec-Test’, Medical Analysis Systems, Inc.), spatial analysis within a GIS, and remote sensing for habitat mapping and characterisation, and geostatistics for estimating vector densities across the landscape from the limited field data. To begin to spatially analyse a mosquito vector, one must understand that insect populations are typically heterogeneous in their spatial densities, responding to multivariate habitat characteristics and environmental controls. Liebhold et al. (1993), for example, provide a detailed review of geostatistical applications for entomological prediction. The application of kriging here provides a means for not constraining the interpolated value of given phenomena (e.g. mosquito abundance), to take advantage of distance and direction in the interpolation process, and to minimise the variance of unexpected error. To capture the spatial dependence of multivariable habitat and environmental conditions, alternate forms of information (besides the primary sample data) can be incorporated into the technique using covariables, a technique known as co-kriging (Isaaks and Srivastava, 1989). Estrada-Pen´a (Estrada-Pena, 1999), for example, used NOAA AVHRR satellite imagery to predict tick abundance and habitat suitability in South America using co-kriging. Further, Nelson et al. (1999) considered the role of geostatistics and GIS in plant disease epidemiology. Their assessment noted the great potential of these techniques to be applied in spatial risk assessment, observing incidences, and understanding spatial-temporal characteristics of disease processes. Remote sensing technology has begun to revolutionise epidemiological research and environmental health practice. Remotely sensed data are highly applicable to regional environmental problems affecting disease outbreak or spread. Digital satellite data have been applied to assess vector habitats for the Rift Valley virus in Central Kenya by Pope et al. (Pope et al., 1992), monitoring rice cultivation and hydrologic environments by Wood et al. (1992) and Washino and Wood (1993), and to map flood boundaries for vector control such as the work of Imhoff and McCandless (1988). Whereas these studies sought to assess vector habitat abundance, this project focused in particular on vectors of West Nile virus, such as Culex pipiens, an abundant and competent summer vector in the humid mid-latitude climate of eastern North America. Nothwithstanding epidemic urban malaria, mosquitoes in the urban United States and Virginia in particular, may be found in a vast array of microhabitats, including forest pools, tree holes, shrub vegetation and foliage, and containers of any human construction (e.g. tyre piles, flower pots, rain gutters, bird baths.) In such extreme and diverse environments without moisture limitations of seasonally wet-dry climates, the eastern United States provides a daunting challenge to the modelling and simulation of mosquito breeding. As a first step to understanding the local spatial ecology of WNV vectors, empirical data, laboratory analyses, and spatial interpolation are combined with remotely sensed data.

2.3 Study area Fairfax County, Virginia and its several smaller surrounding jurisdictions, (Figure 1), is a substantial portion of the western part of Washington, D.C., metropolitan area. Fairfax has the largest county population in the region, approximately 970,000 people in more than 400 square miles (US Bureau of the Census). Coastal lowlands are found east of Interstate-95, with southeasterly drainage into the Potomac River basin and Chesapeake Bay. Soils in this area have moderate to low permeability and relatively high shrink-swell clay properties. A majority of Fairfax County is within the Piedmont physiographic province, having wide, rolling hilltops underlain by weathered metamorphic rocks,

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and dissected into dendritic drainage patterns. The western portions of the County are in the Triassic lowlands of the Piedmont. Poorly drained, clayey soils in the sandstone, conglomerate, siltstone, and shale rock of these basins provides a conducive pedological setting for mosquito breeding. The area is typical of mid-latitude humid subtropical climates, having moderately mild winters and hot, humid summers. In this climate mosquitoes may survive mild winters, and blooms may occur throughout the year but particularly May–October. The field data supporting the analyses are mainly in Fairfax County, but surrounding jurisdictions east of the county will be included in the display of results. Figure 1 Fairfax County study area location and surrounding jurisdictions showing Landsat Tasselled Cap transform wetness band

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3 Methods and analyses 3.1 West Nile virus data Local government officials and public health experts have used several methods to measure and monitor the prevalence of WNV in the region. One simple method is to put chicken flocks in selected locations and test the chickens for the presence of the virus. This study used more plentiful surveillance by mosquito traps (CDC type) collected within Fairfax County, with specimens sorted by species into ‘pools’ for viral testing. An array of serological viral assays and field-based rapid screening tests (e.g. ‘Vec-Test’ ©) were conducted by collaborating personnel at Clarke Mosquito Control Company to identify presence of West Nile virus. Field testing ascertained the presence of viral antigens, but not the relative abundance. On an approximately bi-weekly basis, field personnel would sample light traps and ‘gravid’ traps for mosquitoes. Mosquitoes collected would then be sorted and tested for WNV. ‘Positive pools,’ or field collections of mosquitoes indicating the presence of WNV were identified for mapping and spatial analysis. Twenty-eight pools tested positive for WNV between June 19 and September 9, 2002. These pools included some multiple pools at individual locations using different traps or species of mosquitoes. Of at least 36 possible WNV mosquito vector species (Centers for Disease Control, 2002), the species captured in Fairfax County that tested positive during this period included Culex mosquitoes Cx. pipiens, Cx. restuans, and Cx. Salinarius. Since Culex species are typical of stagnant water, gravid traps are used to trap mosquitoes most competent to transmit disease, such as females recently having a blood meal from a host such as a bird and readying to lay eggs. Pools of approximately 50 mosquitoes, sorted by species, can then inform the manager or analyst of the presence and potential transmission mode (via species ecological traits) in an area. In order to characterise the virus prevalence without biasing pools of different species at redundant locations, the WNV-positive mosquitoes were aggregated by site and date, so that one point location record in the GIS would store the total number of WNV+ mosquitoes across all species for the study duration. Surveillance for dead birds was conducted by both public health and private contractors alongside mosquito vector surveillance. During the 2002 study, 62 dead birds tested positive within the county. Street address was recorded at the time of collection or delivery of each specimen and used as an epidemiological tool for mapping reservoir density and potential vector transmission. The system for collection of dead birds by public health officials depends upon the need and prevalent policy. In general, birds are collected when disease is suspected. Serological and DNA testing later confirms presence/absence of disease (e.g. WNV.) Because WNV was expanding, health officials at the national level would focus on the geographical footprint nationally, but once dead birds or mosquito trap pools test positive, the county would be filled in on the national map and local data would no longer be necessary (from the perspective of state or national surveillance.) However, these local surveillance data could remain informative to the local mosquito and public health officials for actionable responses, providing the budget and analytical personnel were available. In addition, field sampling of dead birds requires public participation by way of reporting. Mapping of mosquito complaint calls (by telephone) is another common means of assessing the abundance of vectors.

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Complaint calls, however, primarily serve as a gauge of ‘nuisance’ levels of mosquitoes. Nonetheless, public officials administering mosquito control programmes will address these by directing surveillance (e.g., traps) and control operations, though they are cognisant that they cannot be entirely driven by complaints, as complaints do not equate to risk of disease transmission. Mosquito control operations are guided geographically by balancing the need for a real coverage of cities or counties with knowledge of local high-abundance vector habitats. Few counties can afford the placement and monitoring of traps in statistically randomised, experimental conditions (mosquito traps require routine inspection, collection, dry ice, batteries and maintenance and land-owner permission.) Five human cases, including two deaths, were confirmed as a result of WNV in Fairfax County in 2002. In addition, two suspected cases of malaria were reported this year, but were not directly tested and studied in this analysis. The residence locations were not precisely mapped for this project owing to confidentiality (although these data are reported and analysed by public health agencies, such as the Centers for Disease Control.) It is possible that these victims contracted the diseases in places far away from their residences, but lacking additional information on the activity patterns and space of the victims, we have to assume that their residential locations and vicinity were the most suspected areas to have contracted disease. Their generalised locations, however, could provide surveillance information in addition to mosquito and bird data, as it would indicate possible disease transmission pathways. In addition, it is not the intention of this research to identify the microscopic landscape and environmental features related to WNV, but to explore approaches to reveal and analyse the landscape and region of patches, neighbourhoods, and county-wide spatial patterns to guide actions. Therefore, data providing generalised locations will be adequate to support the research.

3.2 Kernel density estimation Before applying sophisticated kriging techniques on mosquito data and land cover images to estimate the abundance of the mosquito vectors, we can use the kernel density estimation (KDE) technique to explore the spatial pattern of potential risk for WNV based upon positive dead bird data. Fairfax County has gathered data of dead birds that tested WNV-positive. These data are of great value in supporting this analysis. While chicken flocks are used to monitor the prevalence of WNV, the presence of dead birds testing positive for WNV is also indicative of the presence and spread of the virus among animals in the outdoor environment. Using the dead bird data collected for 2002 in Fairfax County, the KDE method was used to create a density surface of positive dead birds of the region to indicate the ‘intensity’ of the disease. When KDE is employed, it is assumed that the observations (dead birds in this specific application) are merely samples. Thus, the density surface provides a spatial surrogate for comprehensive depiction of the intensity level of the events. Figure 2 shows the locations of the WNV-positive dead birds and the results from the KDE method as a surface mapped with the 2000 census tract boundaries.

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Figure 2 Dead birds density and human transmission 2002, Fairfax County, Virginia

The particular results shown in Figure 2 used a kernel size of 6 km (20,000 feet or approximately 3.8 miles) after experimenting with other kernel sizes. Smaller kernel sizes will create more areas with lower intensity, and vice versa. More detailed discussions on the implications of different kernel sizes can be found in Bailey and Gatrell (1995). Regardless of minor changes in the output patterns in using different kernel sizes, the overall pattern of areas of high abundance (potential ‘hot spots’) versus minimal mosquito abundance (‘cold spots’) do not change significantly. In general, the eastern section of the county had a large cluster of dead birds and thus the intensity of WNV based upon the data was relatively high for that area. Several other smaller clusters also emerged. There

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was one close to the southeast edge of the region, and another one slightly west of the largest cluster. Two other smaller clusters are also discernable on the map. One was in the western portion of the region and another in the northwest part of the county in the Herndon area. Maps of potential risk of disease transmission to humans was next analysed, with the assumption that risk is not uniform spatially within the region. Clusters of high mosquito abundance in proximity to vulnerable human population indicate areas of relatively high-risk for contracting WNV, an inference corroborated by the presence of WNV-positive dead birds. Risk is traditionally defined as the probability that an event, often with unfavourable outcome, will occur (Last, 1988). The unfavourable outcome in the context of this study is disease transmission arising from exposure to noxious agents (Streiner and Norman, 1996) – WNV-positive mosquitoes, whose abundance may also be indicated by the WNV-positive dead-bird population. The level of risk to WNV definitely also varies by different segments of the population, as individuals encounter various risk factors at different levels. These risk factors can include environmental (where they are and where they go) and personal characteristics (demographic indicators and health conditions.) A major risk factor for the contraction of the West Nile virus is exposure to an abundance of the WNV-positive mosquito vectors. Thus, the density surface derived from applying the KDE method to WNV+ dead bird data can depict the spatial variability of level of this risk factor over the region. If one prefers to perform a risk analysis of WNV contraction, it will require detailed data of population exposed to the agents, and the actual number of WNV cases (Moyses and Nieto, 2000). Even though data of human WNV cases are available, they do not really reflect the totality of actual transmission and morbidity because some people contracting the virus are asymptomatic, or may not seek medication attention, and thus represent underreporting. Instead, we perform a qualitative evaluation to examine how well the density surface of WNV+ dead birds can be used as a proxy to indicate the health risk to WNV by spatially comparing the pattern with reported human cases. We overlay the density surface showing the clusters with WNV-positive human cases (Figure 2). The human cases spatially coincided with the high intensity regions very well, a strong indication that positive dead-bird locations correlate with human cases quite well, especially with the larger clusters. Unfortunately, we could not perform a more rigorous quantitative correlation analysis because there were too few human cases. One may argue that the relatively strong spatial correlation between the positive dead birds and human cases is partly due to the ‘biased sampling’ of dead birds because dead birds were recognised and counted only in the populated areas. There were probably more dead birds in the forested and inaccessible areas, but they were not counted. Therefore, more positive dead birds were associated with more people, and the high concentrations of dead birds were a biased observation. Even though the bias argument may likely be true, it still does not invalidate the usefulness of the correlation analysis between the dead birds and human cases. By virtue of relative exposure, virus prevalence in the deep forests or inaccessible areas is of less concern than prevalence of the virus within the neighborhood of our residences or activity space. To further exploit the spatial correlation between the positive dead birds and occurrence of human cases further in the context of disease surveillance and monitoring, one could use the positive dead-bird data and the KDE method to formulate an early warning system to monitor the spread of WNV before striking human (Mostashari et al., 2003;

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National Research Council, 2001). In addition, the results from this analysis can assist the local authority and mosquito control personnel to target areas with high intensity of WNV-cases among the bird population instead of treating the entire region. Therefore, the results have practical value in providing more effective and efficient mosquito control related to the spread of WNV. Unfortunately, some of the counties in the Northern Virginia region are contemplating the idea of not gathering and testing dead birds in the future, partly for financial reasons, and partly because previous WNV-cases already confirmed the presence of the virus in the region. In the light of this possible policy, other techniques not relying on dead-bird data are needed to estimate the risk of contracting WNV.

3.3 Remote sensing and geostatistics This project sought to apply remote sensing and geostatistics to the problem of identifying vector mosquito breeding. Geostatistics offer spatial techniques for interpolating values at unsampled locations, gauging uncertainty in predicted values, or simulating attributes for locations with the limited information available. Field sampling design and collection are labour-intensive and cost-prohibitive tasks. Geostatistics offers a means to sample and estimate the distribution of mosquito vectors more effectively using limited field data augmented by less expensive, but large extent and coverage, digital satellite imagery from Landsat. In this project, a suite of kriging analyses were performed to assess the utility of remote sensing, GIS, and integrated data GIS-RS data for improving the spatial prediction of mosquito abundance. The sampled mosquito pools were aggregated into totals of vector species per site per weekly, monthly, and seasonal abundance. Ordinary kriging (OK) was applied to the mosquito pool dataset alone, to produce a prediction of mosquito abundance. Cressie (1993) notes that co-kriging gives better prediction when several independent variables exist. Thus, in addition to separately applying co-kriging to predict mosquito abundance to gauge the relative contribution of variables (e.g. GIS and image variables), a combined set could be used to assess overall improvement using co-kriging. Co-kriging prediction involved a series of different predictions using the following sets of covariates:

• • • •

mosquito data with DEM elevation and derivatives mosquito data with NDVI and tasseled cap transforms (brightness, greenness, and wetness) mosquito data with Radarsat SAR imagery an integrated set of DEM, model, and image variables.

Maps of mosquito abundance and probability of exceeding a threshold were next output using the ‘Geostatistical Analyst Extension’ within ESRI ArcGIS 8.2. Landsat digital satellite imagery was selected from available summer 2001–2002 scenes for northern Virginia (via USGS Eros Data Center’s Earth Explorer interface.) Landsat’s combination of multispectral passive sensing from visible, near-infrared, and middle-infrared provides a combination of reflectance-based measures for vegetation and surface characterisation at a ground pixel resolution of 30 m 30 m (resampled to same grid resolution as USGS digital elevation models.) In addition, Landsat’s cost is not prohibitive ($600 per scene)

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for 185 km swath. The NDVI derivative of Landsat’s visible red and near-infrared reflectance bands provides one commonly used measure of vegetation vigour. Radarsat data were also assessed in this project courtesy of the University of Maryland Department of Geography. Radarsat is an active microwave sensor, which provides for the assessment of canopy structure and moisture conditions (horizontal-horizontal polarisation at 30 m pixel resolution.) While these data require a remote sensing analyst for operational use, they are examples of satellite image acquisition that are frequently becoming widespread and available for integration and analysis even on GIS computers (e.g. ESRI’s ArcGIS Image Analyst software extension.)

3.4 Image integration Landsat Thematic Mapper (TM) satellite data were acquired from the USGS Eros Data Center for characterisation and analysis of mosquito vector habitats. The image acquired on July 12, 2002, was cloud-free and pre-processed for integration in the GIS. The image was resampled to 30 m 30 m pixel resolution and UTM earth coordinate system using nearest-neighbour interpolation. A set of spectral enhancements were applied, including NDVI, TM4/3 and other band ratios, and the tasselled cap transform (Crist and Ciccone, 1984). Tasselled cap image enhancement was selected for its ability to compress the original spectral bands into three dimensions (brightness, greenness and wetness bands) while retaining straightforward spectral-information value and temporally-constant parameters. The image was subset to the county and a 10 km buffer boundary, in order that the geostatistical algorithms might utilise spectral data from outside the boundary but in proximity. The images were converted from raster format Erdas IMAGINE files to ESRI GRID format without rescaling or histogram transformation (individual bands to separate raster grids) in ArcGIS for analysis using the ESRI Geostatistical Analyst. The exploratory analysis of the mosquito data included techniques to assess the quality of sample distribution, normality, heteroskedasticity, possible data transformation, and identification of outliers or patterns for selection of co-kriging variates. Semivariograms were derived for all input image and mosquito samples. Ordinary kriging of the mosquito point data was compared to iterations of co-kriging with image variables (NDVI, tasselled cap transforms, elevation, slope angle, simulated solar illumination, and topographic wetness potential). Predicted surfaces for mosquito abundances across all species were produced as a first approximation for biweekly, monthly and seasonal totals. Output surfaces were compared in maps for each biweekly, monthly and seasonal totals superimposed over the predicted surfaces. Maps of predicted values, probability maps of exceeding threshold abundance, and prediction standard errors were also produced for validation and sensitivity analysis. Figure 3 is one of several maps generated to explore different kriging methods and image processing techniques. This specific map is the result of using co-kriging methods with the WNV-positive mosquito data and the image data derived from Landsat. Geostatistical analysis includes a variety of means for improving surface prediction, such as data transformation, polynomial detrending, examination of optimal searching and neighbourhood selection, and error modelling to define microscale variations and measurement errors. In using co-kriging to predict values of mosquito abundance, logistics limited the number of sites and temporal frequency of sampling. Cressie (1993) notes that co-kriging gives better prediction when multiple independent variables are available.

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Figure 3 Mosquito abundance derived for co-kriging of WNV-positive mosquito traps and Landsat data

Pixel reflectance values were transformed from digital numbers to calibrated Landsat radiance values and at-satellite reflectance units for calculating the Tasselled Cap transform (c.f. (Crist and Ciccone, 1984; Huang et al., 2002)). This image enhancement was selected for its ability to compress the original spectral bands into three dimensions while also retaining straightforward spectral-information value and temporally-constant parameters (in comparison to scene-dependent PCA) if future comparative or multitemporal imagery were investigated.

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Exploratory analyses of the spatial structure of the field and image data were combined in geostatistical analysis. Semivariograms were analysed to assess the range of spatial dependence, sill, nugget, and possible semivariogram models and trend-removal using the ArcGIS Geostatistical Analyst. Ordinary co-kriging prediction surfaces were generated using semivariogram models with nugget effect and gaussian semivariograms. The Gaussian model reaches its sill asymptotically, and parameter a at 95% of the sill given in Equation (2):

γ (h) = 1 − exp (−

3h2 ). a2

(2)

The Gaussian model is distinguished by its parabolic form near the origin with an inflection point along the range up to the sill of the semivariogram (Isaaks and Srivastava, 1989). In mosquito surveillance, this model is appropriate since distributions tend to be very continuous in the study area, but with potentially extensive areas of low values punctuated by sharp peaks across the landscape. Certainly, this spatial pattern does not preclude isolated, microenvironment-related peaks (such as associated with container-breeding mosquitoes in rain-gutters or an isolated ditch or pool). However, micro-environmental variations are of potentially such narrow range and spatial scale, that they are considered unresolvable and are incorporated by the nugget effect within the model semivariograms. Prediction maps, error, standardised error and plots of error versus predicted and observed values were used in comparing and selecting a final model surface.

3.5 Results evaluation and risk assessment In previous sections, we have used different methods to estimate the geographical distributions of WNV+ mosquitoes. These surfaces can be regarded as proxies of risk of exposing to WNV+ mosquitoes, one of the primary risk factors of contracting WNV. The two approaches, using dead bird data and using landscape data with mosquito field surveillance data, offer different types of risk related to WNV. Their maps are visually different (Figures 2 and 3). But when the values of these maps were scaled to between zero and one, and then compared (Figure 4) and the differences reviewed, the overall discrepancies are not that striking. The differences between the two in the dead-bird clusters are relatively small. Differences between the two increase as the location moves away from the clusters, especially along the edge of the study area. In other words, the two approaches using different data sources provide similar geographical distributions of the potential risks to WNV. However, the presence of WNV-positive mosquitoes or positive dead birds will obviously not pose a human health risk, if no human is present. We know that many types of deadly diseases are likely hidden in deep forests or remote jungles, and they do not pose immediate threats to the well-being of humans or create any real, immediate health risk. In other words, without referring to the context of local human population, the presence or abundance of WNV in a given location, for instance, in the middle of a tropical rainforest less accessible to humans, should not be regarded as a risk factor. Therefore, to complete our risk analysis of WNV in northern Virginia, we have to include the population dimension, especially the residence location of people. Population found

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Figure 4 Map showing the differences between standardised dead bird density (Figure 1) and mosquito abundance (Figure 2)

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within a relatively confined area will have a common level of risk of exposure. However, not everyone at risk in the area is equally vulnerable to WNV. In general, adults with reasonable health conditions with no chronic illness or diseases may not experience any symptoms of WNV even if they have contracted the virus. Therefore, they are not vulnerable to WNV. Besides those with chronic illness, the elderly, infants and toddlers are also relatively vulnerable to WNV in the demographic spectrum (Nash et al., 2001b; Weiss et al., 2001). In addition to the publicly available census of population, we gathered data from other sources to identify locations with high concentrations of vulnerable population groups. For the elderly population, we constructed an address database for various types of elderly facilities based upon information provided by a trade organisation. For the infant and toddler groups, we obtained data from two sources. The Commonwealth of Virginia maintains a database of licensed childcare facilities. Childcare facilities larger than a minimum capacity have to register with the State. We created an address database of these facilities. We also obtained the addresses of all elementary schools in the region from local jurisdictions to compile another address database pertaining to children. The three address databases, elderly facilities, childcare facilities, and elementary schools, were merged and address-matched to identify their locations. These facilities were overlaid with the WNV-positive dead bird density layer. Conceptually, one can create a buffer around these facilities to identify areas of high intensity of WNV based upon the dead bird data in the neighbourhood of facilities. We took a more formal approach to derive a map of risk by taking into account the locations of the vulnerable population. Also adopting the KDE method used early, we created a density surface of the vulnerable population, assuming that the population in those facilities will have a certain degree of mobility. The values of the surface indicate the density of vulnerable population. The density surface is reported in Figure 5 together with the locations of those facilities with high concentrations of the vulnerable population. The surface is quite similar to the surface of bird density, but the highest population density area is east of the high intensity area for the dead birds. The vulnerable population density values were then multiplied separately by the standardised risk levels derived from positive dead-bird data and the standardised risk levels based upon mosquito abundance. Results are reported in Figures 6 and 7. The spatial patterns of the risk level depicted by the two maps are slightly different. While both concentrations of dead birds and vulnerable population are found on the eastern section of the region, they do not spatially coincide very well. Because of the high density of vulnerable population on the east, the overall high health risk was also pulled to the east, implying that mitigation effort, if any, may want to focus in that sub-region. We multiplied the population density by the standardised mosquito abundance indicator, the highest health risk area shifted to the northwest section of the region around the Herndon area. That area has a high density of vulnerable population but a moderate level of mosquito abundance.

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Exploring GIS, spatial statistics and remote sensing for risk assessment Figure 5 Density of vulnerable population

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Figure 6 Risk levels based upon vulnerable population and dead bird density

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Exploring GIS, spatial statistics and remote sensing for risk assessment Figure 7 Risk levels based upon vulnerable population density and mosquito abundance

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4 Conclusion Any type of risk level identified by a certain method always has uncertainty. The uncertainty may be attributable to the quality of the data supporting the analysis, or due to the nature of the problem, which involves multiple facets, which are not fully captured by a specific method. In this paper, we demonstrated the possibility of using multiple approaches in identifying the risk of contracting WNV. The two approaches offer different results, but provide a more thorough assessment of the health risk factors. If results from alternate approaches are taken into account fully by risk management personnel to formulate policy, measures or actions, the risk factors will be handled more comprehensively. Geostatistical methods in general, and KDE and kriging in particular for this project, are invaluable tools to analyse the distributions of population and disease vectors. While the distributions of population, vectors, or incidences are spatially discontinuous, and hamper spatial visualisation and identification of spatial patterns, if any, geostatistical tools treat these locations as sample locations, and estimate a surface to depict the spatial pattern of the population. Rendering of these statistical surfaces will assist the formulation of hypotheses and identify abundant ‘hot spots’ for further investigations. Our results also affirm the potential role of remote sensing imagery for local surveillance and control of mosquito vectors. GIScience continues to evolve capabilities to manipulate and augment vector analyses with raster and remotely sensed data (such as geostatistical tools coupled with GIS). The potential having been illustrated, access and cost of imagery, protocols for integration of remote sensing and GIS field data, and training for professionals in mosquito control remain the primary constraints to adoption and operational use of remote sensing techniques demonstrated in this project. The resolution of Landsat ETM+ provided an improvement to the spatial grain of mosquito patterns achieved only by the interpolation of field data. This improvement must be further quantified and compared to what is achievable by moderate resolution sensors developed for earth science or meteorological use (e.g. MODIS or AVHRR) that have superior temporal resolution and extent or also higher spatial resolution sensors with variable costs and availability (e.g. IKONOS). Since mosquito surveillance and control activities are primarily the provenance of local institutions and professionals working in public health and utilities departments, the resolution of Landsat demonstrated here offers a balance between the temporal and spatial needs of mosquito vector surveillance and control. There are some studies on the environmental aspects of WNV and attention to early season bird deaths as sentinels (Guptill et al., 2003). Research investigations lack the framework to assess health risk and exploit the environmental factors for risk mitigation. Even though this paper focuses narrowly on the analysis of WNV, the approaches suggested are applicable to the study of other vector-borne diseases, such as the Lyme disease (Dister et al., 1997), highly dependent upon the landscape characteristics. One can explore the situation by analysing the incidence data using the KDE method. Remote sensing techniques can be used to identify areas that provide the habitat for the vectors or in conjunction with environmental models, such as simulated surface wetness (Shaman et al., 2002). Field data gathered by various survey methods can be integrated with remote sensing using kriging techniques. After identifying areas with possible high

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vector concentration, the population at risk has to be brought into the picture to evaluate the health risk to humans. This general methodology has to be tailored for different vector-borne diseases, but the overall idea is not disease-dependent.

Acknowledgements The authors acknowledge the advice of three anonymous reviewers and Mr C. John Neely of Clarke Mosquito Control. The research was supported by NAG-13–01009 ‘Virginia Access’ to George Mason University and Old Dominion University.

Note The Landsat-7 Enhanced Thematic Mapper + sensor used in this research had developed a scan-line detector malfunction in May 2003. Following NASA investigations, limited data are being reprocessed and distributed at the time of this writing. Prospective users may check the Landsat-7 Homepage at NASA Goddard Space Flight Center for updates on the sensor and enhancements to the products (http://landsat7.usgs.gov/ slc_enhancements/).

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