Louisa R. Beck

Predicting the Risk of Lyme Disease: Habitat Suitability for Ixodes scapularis in the North Central United States

The distribution and abundance of Ixodes scapularis were studied in Wisconsin, northern Illinois, and portions of the Upper Peninsula of Michigan by inspecting small mammals for ticks and by collecting questing ticks at 138 locations in state parks and natural areas. Environmental data were gathered at a local level (i.e., micro and meso levels), and a geographic information system (GIS) was used with several digitized coverages of environmental data to create a habitat profile for each site and a grid map for Wisconsin and Illinois. Results showed that the presence and abundance of I. scapularis varied, even when the host population was adequate. Tick presence was positively associated with deciduous, dry to mesic forests and alfisol-type soils of sandy or loam-sand textures overlying sedimentary rock. Tick absence was associated with grasslands, conifer forests, wet to wet/mesic forests, acidic soils of low fertility and a clay soil texture, and Precambrian bedrock. We performed a discriminant analysis to determine environmental differences between positive and negative tick sites and a regression equation to examine the probability of I. scapularis presence per grid. Both analyses indicated that soil order and land cover were the dominant contributors to tick presence. We then constructed a risk map indicating suitable habitats within areas where I. scapularis is already established. The risk map also shows areas of high probability the tick will become established if introduced. Thus, this risk analysis has both explanatory power and predictive capability.

Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands

BackgroundIn the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands.MethodsPanchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types.ResultsThe misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3–22.7% for aquatic habitats, and 18.1–25.1% for anopheline-positive larval habitats.ConclusionOne-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.

Kriging in the shadows: geostatistical interpolation for remote sensing

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the methods practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Estimating high mosquito-producing rice fields using spectral and spatial data

Abstract The cultivation of irrigated rice provides ideal larval habitat for a number of anopheline vcclors of malaria throughout the world. Anopheles freeborni, a potential vector of human malaria, is associated with the nearly 240 000 hectares of irrigalcd rice grown annually in Northern and Central California; therefore, this species can serve as a model for the study of rice field anopheline population dynamics. Analysis of field dala revealed that rice fields with early season canopy development, that are located near bloodmcal sources (i.e., pastures with livestock) were more likely to produce anopheline larvae than fields with less developed canopies located further from pastures. Remote sensing reflectance measurements of early-season canopy development and geographic information system (GIS) measurements of distances between rice fields and pastures with livestock were combined to distinguish between high and low mosquito-producing rice fields. Using spectral and distance measures in cither a dis...

Distinguishing high and low anopheline-producing rice fields using remote sensing and GIS technologies

Abstract Worldwide, 140 million ha are devoted to rice cultivation, mostly in developing countries of the tropics and subtropics where malaria still constitutes a serious human health problem. Because rice fields are flood-irrigated on a semi-permanent basis during each growing season, they provide an ideal breeding habitat for a number of potential mosquito vectors of malaria. One of these vectors, Anopheles freeborni , is distributed throughout nearly 240 000 ha of irrigated rice in northern and central California, and may serve as a model for the study of rice field mosquito population dynamics using spectral and spatial information. Analysis of field data revealed that rice fields with rapid early season vegetation canopy development, located near livestock pastures (i.e. bloodmeal sources), had greater mosquito larval populations than fields with more slowly developing vegetation canopies located further from pastures. Remote sensing reflectance measurements of early season rice canopy development and geographic information system (GIS) measurements of distance to livestock pasture were combined to distinguish between high and low mosquito-producing rice fields. These distinctions were made with 90% accuracy nearly 2 months before anopheline larval populations peaked.

Spectral and spatial characterization of rice field mosquito habitat

Abstract Irrigated rice provides an ideal breeding habitat for Anopheles freeborni, the western malaria mosquito, throughout California. In a 1985 study, it was determined that early-season rice canopy development, as monitored using remotely sensed data, could be used to distinguish between high and low mosquito producing rice fields. This distinction could be made over two months prior to peak mosquito production. It was also found that high-producing fields were located in an area characterized by a diversity of land use, including livestock pastures, whereas the low-producing fields were in an area devoted almost exclusively to the cultivation of rice. The ability to distinguish between high and low mosquito producing fields prior to peak mosquito production is important in terms of mosquito habitat surveillance and control.

A remote sensing and geographic information system approach to sampling malaria vector habitats in Chiapas, Mexico

This paper describes a procedure whereby remote sensing and geographic information system (GIS) technologies are used in a sample design to study the habitat of Anopheles albimanus, one of the principle vectors of malaria in Central America. This procedure incorporates Landsat‐derived land cover maps with digital elevation and road network data to identify a random selection of larval habitats accessible for field sampling. At the conclusion of the sampling season, the larval counts will be used to determine habitat productivity, and then integrated with information on human settlement to assess where people are at high risk of malaria. This aproach would be appropriate in areas where land cover information is lacking and problems of access constrain field sampling. The use of a GIS also permits other data (such as insecticide spraying data) to the incorporated in the sample design as they arise. This approach would also be pertinent for other tropical vector‐borne diseases, particularly where human activ...

Medsat: A satellite system for surveillance of tropical vector‐borne diseases

In this paper, the authors describe the need for, and preliminary student design of, a research satellite system (Medsat) devoted to the surveillance of environmental and epidemiological factors that influence the patterns and dynamics of tropical vector‐borne diseases.