title: Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.
abstract: BACKGROUND
  Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae.
  
  
  METHODS
  Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC).
  
  
  RESULTS
  All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable.
  
  
  CONCLUSIONS
  Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.
authors: Department of Vector Ecology and Environment, Institute of Tropical Medicine (NEKKEN), Nagasaki University, Nagasaki, Japan.
authors: Jephtha C
authors: JC
authors: Nmor
authors: Toshihiko
authors: T
authors: Sunahara
authors: Kensuke
authors: K
authors: Goto
authors: Kyoko
authors: K
authors: Futami
authors: George
authors: G
authors: Sonye
authors: Peter
authors: P
authors: Akweywa
authors: Gabriel
authors: G
authors: Dida
authors: Noboru
authors: N
authors: Minakawa
mesh_terms: D000349:Africa / Q000453:epidemiology; D000818:Animals; D000852:Anopheles / Q000502:physiology*; D003625:Data Collection; D017753:Ecosystem; D040362:Geographic Information Systems; D005843:Geography; D006801:Humans; D007303:Insect Vectors / Q000502:physiology*; D007630:Kenya / Q000453:epidemiology; D007814:Larva; D016015:Logistic Models; D008288:Malaria / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D008962:Models, Theoretical; D009032:Mosquito Control; D058998:Remote Sensing Technology; D015203:Reproducibility of Results; D012621:Seasons