Background: In 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. Methods: Panchromatic 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. Results: The 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. Conclusion: One-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.

The development of interoperable environmental process models is widely supported by both the research community and government agencies. This paper discusses a conceptual framework to guide the delineation of process components, the basic unit of interoperable models. The concepts of semantic reference system and dual hierarchy are explored to establish the conceptual framework. Based on this discussion, the Formal Concept Analysis is used as an analytical approach to decompose and fuse existing environmental process models in order to identify environmental process components. For purposes of discussion, a hydrological process, surface runoff, is used throughout the paper as an example of an environmental process to support the conceptual discussion and to illustrate the application of the conceptual framework and analytical approach.

Recent health threats, such as the SARS, H1N1, and ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics. We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial–temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic. Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.