INTRODUCTION
During the past two decades, remotely sensed data have been used to describe and predict geographical and temporal patterns in vector-borne disease transmission and disease prevalence.1,2 Studies mapping malaria vector mosquito breeding habitats, transmission, or disease have been made in Africa,3–7 selected regions of South and Central America,8–12 and Asia.13,14 Reliable information about vector density and malaria transmission risk is essential for understanding variations in local disease epidemiology and to stratify intervention programs. The most extensive work on geographical variation of malaria risk in Africa has been made at a continental scale based on meteorological data and historical ground data from various sites across the continent.3,6 Low spatial and high temporal resolution meteorological satellite imagery has been used to predict malaria seasons and severity of disease in Kenya.5 These are useful tools for describing the general continental and national trends in malaria risk, but they are not appropriate for describing local-scale variation in disease patterns often seen just few kilometers apart. High local variation in malaria epidemiology is particularly common in the Sahel region of Africa, where malaria is characterized by very focal and seasonal transmission.7,15–21 At this scale, data acquired by high spatial-resolution optical sensors on Earth observation satellites has been used to map mosquito breeding habitat.4,7–9 In this study, we extend our previous work in The Gambia7,22–24 to encompass a large area (2500 km2), with consequent environmental heterogeneity. We then combine these findings to produce a map of predicted entomological inoculation rates across the entire area.
The Gambia in West Africa is typical of the Sahel region. Two characteristic soil types predominate: (1) free-draining sand laterite covered with open woodland savannah or farmland and (2) water-retaining, alluvial deposits along The River Gambia with marsh vegetation, mangrove forest, or rice cultivation.25 The malaria transmission season is confined largely to the short rainy season and is brief but intense from August to the beginning of December, after which mosquito populations rapidly decline.22,23,26 The most important malaria vectors are Anopheles gambiae sensu stricto, Anopheles melas, and Anopheles arabiensis,27, all members of the An. gambiae complex.28 Large geographical variation in the epidemiology of malaria has been demonstrated among different parts of the country18,29,30 and within small areas.7,24,26 The present study was a part of a larger investigation designed to identify the major factors governing local-scale variation in malaria transmission and infection in The Gambia.7,22–24,26,31–33 In earlier work, we demonstrated that nearly all vector breeding sites are found on the landward edge of the flooded alluvial plains bordering the river.7,23 Larval surveys in the study area revealed that there was a strong association between certain land cover classes and the presence of larvae.23 An. gambiae s.s. and An. melas were breeding from the edge of the alluvial deposits to ~ 1.5 km into the flood plain, while An. arabiensis was mainly found breeding in the rice nurseries on the very edge of the alluvium. No breeding was found on the sandy laterite soils.23
MATERIALS AND METHODS
Study area and selection of villages.
The study area was located in the central region of The Gambia, defined by the border to Senegal and the UTM (zone 28) grids 410000mE and 480000mE from the national 1:50,000 survey maps34 (Figure 1). The study villages were selected as a geographically stratified random sample of all villages located in the study area by dividing the area into 8 sectors of approximately equal size and selecting 6 villages at random in each. All towns and villages in the study area were eligible for inclusion in the study irrespective of size. Thus, a total of 48 villages were selected for the 1996 surveys. In 1997, entomological surveys were conducted in a smaller random sample of 21 villages, of which 9 were repeat villages from 1996 to examine variation between years. The selected villages varied in size from 50 to 10,000 inhabitants. In the field, village positions (approximate center) were confirmed using differentially corrected global positioning system (GPS) data (GeoExplorer II, Trimble Navigation Limited, Sunnyvale, CA).
Adult mosquito collections and laboratory analysis.
Biweekly mosquito collections were made in all study villages throughout the rainy seasons of 1996 and 1997 (mid-July to mid-December). Collections were made using 2 CDC miniature light traps operating from 7 PM to 7 AM in bedrooms of single occupants living in the part of the village closest to the swamp zone. Light trap catches are routinely used in many parts of Africa for estimating EIRs, as they have been shown to be directly comparable with human landing catches.35 Mosquitoes were transferred to the laboratory for species identification and further investigations. In the laboratory, mosquitoes were separated by sex and identified on the basis of morphologic characters,28 and up to 220 mosquitoes per trap were stored in containers with silica gel for later P. falciparum sporozoite analysis.36
Survey of larval breeding habitats.
A detailed description of this survey is given elsewhere,24 but a summary is provided here. To describe the seasonal and spatial variation in the breeding of the key malaria vector species, two types of larval surveys were carried out during the rainy seasons of 1996 and 1997. The primary method was larval sampling and habitat description at 100-m intervals along four transects, starting from representative study village and continuing toward the river. To confirm that transects were representative of the study area, larval sampling was also carried out in a variety of aquatic habitats in different parts of the study area during the rainy seasons of 1996 and 1997. Sampling was focused in the floodplains and in the natural depressions on the periphery and outside areas with alluvial deposits. During both types of collections, the specific habitats were described and samples of the predominant plant species were taken and preserved for later identification. Locations of all larval collection points were determined using differentially corrected GPS positions.
Mapping of Anopheles breeding sites using remote sensing.
To map the habitats that represent breeding areas of vector mosquitoes, we analyzed a LANDSAT TM image acquired from November 1992 (late rainy season). Few cloud-free images are available for the area during the rainy season, and this image was the most contemporary with the fieldwork. This image had no cloud cover and covered the entire study area. LANDSAT TM measures reflected radiation in seven spectral bands from visible to infra-red (30-m spatial resolution) and thermal (60 m). Using a geographical information system (GIS) (ArcInfo: ESRI, Redlands, CA), the image was georeferenced to UTM Zone 28 based on the 1:50,000 national survey maps of The Gambia,34 which were originally developed from aerial photography. Additional features were also digitized from the maps, including main roads, national borders, edges of flood plains, and villages. The locations of all the larval survey points were added to the GIS to assist with defining training sites for image classification. We identified 10 classes of land cover classification: (1) open mud flat; (2) mangrove (Avicennia sp. and Rhizophera sp.); (3) terrestrial forest; (4) open water (river and lakes); (5) terrestrial grasslands; (6) rice cultivation (Oryza sativa L.); (7) sea-purslane (Sesuvium sp.)-dominated wetland; (8) wetland dominated by water-tolerant grasses (Paspalum sp. and Sporobolus sp.); (9) wetland dominated by sedges (Eleocharis sp.); and (10) swamp fringes (mudflat, often with hoof prints and sparse, low-growing vegetation). Image classification was undertaken using ERDAS Imagine 8.3 software (Leica Geo-systems, Atlanta, GA) using LANDSAT TM bands 1 through 5. Image processing of the satellite image was limited to the area within the floodplain because (1) our surveys showed that nearly all larvae were produced there7,23 and (2) a preliminary unsupervised classification (using the ISODATA algorithm) of the image showed that burned areas of terrestrial grasslands had similar spectral reflectance signatures to wet-land habitats. Therefore, within this zone, a maximum-likelihood supervised classification was conducted using 54 training polygons of known land cover types. Validation was conducted using 240 independent point samples to compare the known and classified land cover; validation statistics included overall accuracy and the calculation of the kappa statistic. The kappa statistic accounts for the fact that even a completely random assignment of pixels to classes will produce some correct values. Thus, kappa is a measure of the actual agreement between reference data and the classification and the chance agreement between the reference data and a random classifier.37 For analysis, the classified image was converted to an ArcInfo grid coverage with 30-m grid cell length.
Spatial and statistical analysis.
The geometric mean (GM) number of mosquitoes was calculated as described in the formula below, where xi is the number of An. gambiae s.l. in a catch and n is the total number of catches.
The mean monthly sporozoite rate in each village was calculated as the total number of positive mosquitoes divided by the total number of ELISA-analyzed specimens per month. The monthly EIR was calculated as the mean monthly sporozoite rate multiplied by the GM number of mosquitoes multiplied by the number of days in the month. Catches performed in the first and last month of the surveys did not cover the entire month, but the data collected was considered as such. The monthly EIR from July to December was totaled to give the rainy-season EIR for that year.
Larval habitat closer to a village is likely to be the source of more mosquitoes than an identical habitat further away. To account for this, we weighted grid cells of breeding habitat by distance from sample villages as a function of distance. Using GIS tools, we counted the numbers of grid cells containing larval habitat within 50 concentric circles around each village, at 100-m intervals up to 5 km from each village (here considered the maximum flight range of the mosquitoes). The count of grid cells of larval habitat in each distance band was weighted for distance from village as 1/d2, where d was measured in 100-m distance bands. Thus, d = 1 for grid cells from 0 to 100 m; d = 2 for grid cells from > 100 to 200 m, and so on. Use of 100-m band classes, rather than meter units, prevented large values in the denominator and simplified the GIS algorithm.
The relationship between distance weighted area of breeding habitat and rainy-season EIR was described by linear regression, with both dependent and independent variables transformed to normality by natural logarithms, thus:
where
and m is the slope coefficient, d is the distance class from target village to pixels of larval habitat (in intervals of 50 m from 1 to 5000 m, where 1 = 0–50 m, 2 = 51–100 m, . , 50 = 4951–5000 m), H is the number of pixels of larval habitat in that distance class, and c is a constant.
Because the villages were geographically randomly sampled and breeding habitat was distributed across the study area, we did not include a spatial-autoregressive term in the model. However, we did test for spatial autocorrelation in regression-model residuals using the Rookcase program.38 Statistical analysis was carried out using SPSS software (version 11.0, SPSS Inc., Chicago, IL).
RESULTS
During 1996 and 1997, a total of 1344 light trap collections were made. These yielded 78,775 An. gambiae s.l., of which 65,523 were analyzed for P. falciparum sporozoites using the ELISA technique.
Mosquito density in the 48 villages surveyed in 1996 showed significant variation from 0 to 290 female An. gambiae s.l. per night per house (mean/village GM = 46.25). The number of mosquitoes in each village declined the further the village was from the edge of the alluvial deposits (Figure 2). There was no statistically significant difference in mosquito abundance in each village from 1996 to 1997 (N = 9, Wilcoxon signed ranks test, P = 0.86).
The mean monthly sporozoite rate for villages was low (mean = 1.50%, SD = 1.41%, min = 0, max = 6.72%) and uncorrelated with GM mosquito numbers (rs = −0.246, P = 0.093). EIR varied from 0 to 166 infective bites per person per rainy season (mean GM = 47.4, SD = 43.4). There was no significant difference between overall mean EIR in 1996 and 1997 (t = −0.630, df = 67, P = 0.531) or between years in the subset of 9 villages measured in both years (paired t test, t = 1.047, df = 8, P = 0.326).
The 10-category classification of land cover within the alluvial zone had an overall accuracy of 69%, with a kappa statistic of 0.65. Larval surveys identified that five land covers contained An. gambiae s.l. larval habitats in the study area: natural wetland dominated by water-tolerant grasses, seapurslane, sedges, areas of rice cultivation, or swamp fringes. We aggregated these five mosquito-producing classes into a single class and the remaining 5 non-mosquito-producing classes into a second class to produce a binary map of larval and non-larval habitats (Figure 3). The aggregated classified image had an overall accuracy of 85% and a kappa statistic of 0.7, which indicates a very good classification. The higher overall accuracy is a result of the reduced number of classes because any misclassification within the individual mosquito producing or non-mosquito-producing classes does not affect accuracy.
The GM of mosquito abundance in villages was strongly positively correlated with the amount of distance-weighted breeding habitat surrounding them (rs = 0.793, P < 0.001). The GM vector abundance and EIR in the villages increased linearly with the amount of surrounding, distance-weighted breeding habitat (Figure 4).
A linear regression of the natural logarithm of the sum of distance-weighted breeding habitat against the natural logarithm of EIR in villages was highly significant and explained 54% of the variance observed among the 48 villages (r = 0.733, P < 0.001).
There was no significant spatial autocorrelation in standardized residuals (Moran’s I = 0.034, z = 0.275, P = 0.392), indicating that there were no spatial trends in the prediction. We used this regression equation to the map the predicted mean monthly EIR (Figure 5) as a 30-m grid cell length surface of the study area. Predicted EIR values and the actual EIR measured in the 12 new villages in 1997 were highly correlated (r = 0.752, P = 0.005).
DISCUSSION
An understanding of the distribution of vector breeding habitats, dispersal of adult mosquitoes, and the resulting risk of malaria transmission are important considerations when designing malaria intervention programs and trials. However, obtaining reliable and consistent data over large areas is a difficult task. Project planners are often left to make general assumptions about transmission risk or are forced to disregard variations altogether. In this paper, we map local-scale (village-scale) variation in vector abundance and EIR over a large area, in a region containing patchy and relatively localized breeding habitats for three of the most important malaria vector species on the African continent.
Previous studies in The Gambia have demonstrated variation in the abundance of mosquitoes between localities in close proximity.7,18,26,39 The present study extends this work to encompass a 2500-km2 area, larger than the geographical extent typically considered in intervention trials, and is based on extensive entomological data collected in a large sample of spatially stratified locations. Rogers and colleagues2 argue that the main benefit of using remote sensing is to develop predictive forecasting systems for malaria outbreaks in epidemic prone regions. We also advocate satellite imagery as a planning tool to map local-scale variation in transmission in areas of transmission where production of vectors can be related to land-surface properties detectable by remote sensing. Our resulting map (Figure 5) stratifies the landscape into zones within which villages experience an estimated EIR over a complete rainy season. The strong correlation between predicted EIR values and those actually measured in 1997 indicate the potential of static risk maps as a useful guideline when planning intervention programs and trials (indeed, our results are already in use in current trials in The Gambia). Contemporary cloud-free images are often difficult to obtain and may limit the usefulness of optical imagery, such as LANDSAT ETM/TM or SPOT-XS, in real-time risk forecasting. Also, in many situations, the rate of optical image acquisition is slower than the pace of seasonal and environmental changes. Recently, interest has developed in the capabilities of radar satellite imagery to provide frequent, high-resolution mapping of mosquito habitat40–42 because microwave frequencies are not limited by cloud cover and are particularly sensitive to open water and the inundation status of vegetation. Synthetic aperture radar (SAR) imaging provides an opportunity to not only obtain a contemporary snapshot of habitat but also to map a dynamic landscape in which mosquito habitat availability changes over the course of the wet season, which is information that may explain some of the seasonal variation in vector abundance or EIR in villages. However, technical challenges in the use of SAR, particularly “layover” in hilly terrain and “speckle” caused by random interference from the multiple scattering returns, present significant problems for mapping Anopheles larval habitat in many settings.
Our studies of the breeding habitats of An. gambiae s.l. showed that almost all vector mosquito production within the study area comes from the flooded alluvial soil bordering The River Gambia.23 The sharp decline in mosquito density away from mosquito-breeding sites highlights the significance of these areas in understanding the microepidemiology of malaria transmission and epidemiology in relation to flood-prone river systems. Because very little breeding of vector mosquitoes takes place outside these areas, the abundance of vector mosquitoes away from these sites is a direct result of dispersal and survival of the vector mosquitoes. In a mark/ release/recapture study in the central part of The Gambia, 16% to 20% of recaptured An. gambiae s.l. were found in neighboring villages 1–1.5 km away.43 These results are consistent with findings from a rural area of Burkina Faso with Sudan Savannah, in which Constantini and colleagues44 showed that An. gambiae s.l. traveled 350–650 m/day with a survival of 73% to 89% per day. These findings indicate that the flight range and survival of An. gambiae s.l. in The Gambia are comparable with those from Burkina Faso and may thus be broadly characteristic of the species in areas of open savannah where settlements are widely scattered. In areas with a high population density and spatial coverage of hosts, the flight range is likely to be lower. Indeed, transmission risk is seen to decrease over very short distances in peri-urban settings.45,46 We modeled mosquito abundance in villages as an inverse square function of proximity to larval habitat. Consequently, both vector density and EIR decrease rapidly when moving away from breeding areas. The fact that this function provided a good statistical fit to the observations is consistent with the hypothesis that Anopheles gambiae s.l. disperse in nonlinear, local movements, but this was not tested directly.
There was a strong negative gradient in EIR away from the breeding sites. Within 4 km from the breeding sites, there was an 11.5-fold reduction in the mean number of infective bites received per person. In the absence of protective measures such as bed nets, people living near the flooded alluvial soils were therefore at much higher risk of getting infective bites compared with people living in villages further from the alluvium. This decrease in transmission intensity away from the breeding sites was mainly caused by a drop in the number of mosquitoes and not by differences in their infectivity.22 Similar sharp gradients in transmission intensity have been demonstrated in urban and peri-urban areas44–46 and in mountainous areas.47,48 This study demonstrates that strong transmission gradients also occur in open flat areas when mosquito breeding is localized.
Investigations carried out in the same villages in 199624 demonstrated that use of bed nets was higher near the river than further away, confirming the findings from previous studies.18,49 The use of bed nets is one component of vulnerability to receiving an infective bite, which, when combined with EIR hazard, constitutes a measure of infection risk. Because bed nets do reduce transmission,39 children in villages close to the river will in reality receive significantly fewer infective bites than is indicated from the light trap collections in this study. Despite this potential dampening of the effective transmission gradient, the parasite prevalence was significantly lower in villages far from the breeding sites compared with villages close to the breeding sites. However, clinical illness followed the opposite trend, being more common in children less exposed to infection further away from the river.24 Our findings highlight the spatial complexity of transmission, infection, and clinical disease at local scales.
We have shown that malaria vector breeding in The Gambia is very localized and that it is possible to map the resulting variation in vector density and transmission risk at a fine spatial resolution. Many parts of the Sahel region are dominated by large river systems in otherwise arid areas. Because malaria transmission has often been related to these riparian habitats and associated rice fields,17,19,21,50,51 similar steep gradients in transmission intensity are likely to be seen in association with these areas. When planning malaria intervention programs and intervention trials in areas with such highly focal transmission, programs are likely to benefit from taking into account the geographical variation in transmission intensity. Continued development of large-scale geographical approaches, including earth observation techniques, can be of significant aid in targeting risk areas and stretching limited resources. However, to fulfill the potential of this technology, we need to fill gaps in our knowledge of the basic landscape ecology of the most important malaria vector species and the complex relationship between exposure and clinical disease.
Study area in the central part of The Gambia, showing the 48 villages studied during 1996 (•) and the 21 studied in 1997 (○), with 9 villages studied in both years. The River Gambia is shown in dark gray, and floodplain alluvial deposits are cross-hatched. The national border with Senegal is shown as a solid line. Inset: Location of study area within The Gambia (white) surrounded by Senegal (gray).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Study area in the central part of The Gambia, showing the 48 villages studied during 1996 (•) and the 21 studied in 1997 (○), with 9 villages studied in both years. The River Gambia is shown in dark gray, and floodplain alluvial deposits are cross-hatched. The national border with Senegal is shown as a solid line. Inset: Location of study area within The Gambia (white) surrounded by Senegal (gray).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Study area in the central part of The Gambia, showing the 48 villages studied during 1996 (•) and the 21 studied in 1997 (○), with 9 villages studied in both years. The River Gambia is shown in dark gray, and floodplain alluvial deposits are cross-hatched. The national border with Senegal is shown as a solid line. Inset: Location of study area within The Gambia (white) surrounded by Senegal (gray).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
GM of female An. gambiae s.l./night in 48 villages sampled in 1996 in relation to Euclidean distance from each village to the nearest alluvial deposits along the river.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
GM of female An. gambiae s.l./night in 48 villages sampled in 1996 in relation to Euclidean distance from each village to the nearest alluvial deposits along the river.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
GM of female An. gambiae s.l./night in 48 villages sampled in 1996 in relation to Euclidean distance from each village to the nearest alluvial deposits along the river.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
(a) An. gambiae s.l. breeding habitat (in red) mapped by supervised classification of bands 1 through 5 of a LANDSAT TM image from the late rainy season, November 1992. Villages sampled in 1996 are shown (•); (b) area shown at larger scale in the dashed line in (a).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
(a) An. gambiae s.l. breeding habitat (in red) mapped by supervised classification of bands 1 through 5 of a LANDSAT TM image from the late rainy season, November 1992. Villages sampled in 1996 are shown (•); (b) area shown at larger scale in the dashed line in (a).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
(a) An. gambiae s.l. breeding habitat (in red) mapped by supervised classification of bands 1 through 5 of a LANDSAT TM image from the late rainy season, November 1992. Villages sampled in 1996 are shown (•); (b) area shown at larger scale in the dashed line in (a).
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Amount of breeding habitat within 5 km of village estimated from a classified LANDSAT TM image and weighted for distance from village by inverse square distance versus (a) GM of female An. gambiae s.l./night in 48 villages sampled in the rainy season of 1996 and (b) rainy-season EIRs for these villages in 1996.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Amount of breeding habitat within 5 km of village estimated from a classified LANDSAT TM image and weighted for distance from village by inverse square distance versus (a) GM of female An. gambiae s.l./night in 48 villages sampled in the rainy season of 1996 and (b) rainy-season EIRs for these villages in 1996.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Amount of breeding habitat within 5 km of village estimated from a classified LANDSAT TM image and weighted for distance from village by inverse square distance versus (a) GM of female An. gambiae s.l./night in 48 villages sampled in the rainy season of 1996 and (b) rainy-season EIRs for these villages in 1996.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Predicted rainy-season (July to mid-December) EIR in central Gambia, based upon the distribution of An. gambiae s.l. breeding habitat mapped using LANDSAT TM imagery. The River Gambia is shown in black; gray line is the international border with Senegal; solid circles are villages.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Predicted rainy-season (July to mid-December) EIR in central Gambia, based upon the distribution of An. gambiae s.l. breeding habitat mapped using LANDSAT TM imagery. The River Gambia is shown in black; gray line is the international border with Senegal; solid circles are villages.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Predicted rainy-season (July to mid-December) EIR in central Gambia, based upon the distribution of An. gambiae s.l. breeding habitat mapped using LANDSAT TM imagery. The River Gambia is shown in black; gray line is the international border with Senegal; solid circles are villages.
Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 76, 5; 10.4269/ajtmh.2007.76.875
Address correspondence to Claus Bøgh, P.O. Box 2148, Kuta 80361, Bali, Indonesia. E-mail: cbogh@cbn.net.id
Authors’ addresses: Claus Bøgh, P.O. Box 2148, Kuta 80361, Bali, Indonesia, Telephone: +62 (0) 361 757149, E-mail: cbogh@cbn.net.id. Steven W. Lindsay, School of Biological and Biomedical Sciences, Durham University, South Road, Durham DH1 3LE, UK, Telephone: +44 (0) 191 334 1349, E-mail: s.w.lindsay@dur.ac.uk. Christopher J. Thomas, CIRRE, Institute of Rural Sciences, University of Wales Aberystwyth, United Kingdom, E-mail: cjt@aber.ac.uk. Siân E. Clarke, Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, Bedford Square, London, WC1B 3DP, UK, Telephone: +44 (0) 20 7299 4642, E-mail: sian.clarke@lshtm.ac.uk. Andy Dean, Hatfield Consultants, 201-1571 Bellevue Avenue, Vancouver, BC, Canada, Telephone: +1 (604) 926 3261, E-mail: adean@hatfieldgroup.com. Musa Jawara and Margaret Pinder, Medical Research Council Laboratories, P.O. Box 273, Banjul, The Gambia, Telephone: +220 4495442, E-mails: mjawara2000@yahoo.co.uk and mpinder@qanet.gm.
Acknowledgments: The authors thank The Medical Research Council Laboratories in The Gambia for providing laboratory facilities for the project. Special thanks go to Pate Makalo, Ngansu Tourey, Seleman Bah, Yaya Bah, Fabakari Sanyang, Joseph Bas, Karang Njey, and Lamin Bojang for excellent assistance during the field and laboratory work in The Gambia. We also thank Danida (RUF), Knud Højgaards Fond, and DBL-Institute for Health Research and Development for providing funds and facilities for the project. Special thanks go to Henry Madsen for statistical advice and to Henrik Stolpe and Bennedikte Loehr for laboratory work done at The Danish Bilharziasis Laboratory.
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