BackgroundIdentification 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.MethodsUsing 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).ResultsAll 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.ConclusionsBoth 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.

Background: Measles remains a serious vaccine preventable cause of mortality in developing nations. Vietnam is aiming to achieve the level of immunity required to eliminate measles by maintaining a high coverage of routine first vaccinations in infants, routine second vaccinations at school entry and supplementary local campaigns in high-risk areas. Regular outbreaks of measles are reported, during 2005-2009.Methods: National measles case-based surveillance data collected during 2005-June 2009 was analyzed to assess the epidemiological trend and risk factors associated with measles outbreak in Vietnam.Results: Of the 36,282 measles suspected cases reported nationwide, only 7,086 cases were confirmed through laboratory examination. Although cyclical outbreaks occurred between 2005 and 2009, there was no definite trend in measles outbreaks during these periods. Overall, 2438 of measles confirmed cases were among children ≤5 years and 3068 cases were among people ≥16 years. The distribution with respect to gender skewed towards male (3667 cases) significant difference was not observed (P= 0.1693). Unsurprisingly, 4493 of the confirmed cases had no history of vaccination (X2 <0.01). The northern and highland regions were identified as the main endemic foci and the spatial distribution changed with time. The occurrence of cases, in a considerable proportion of vaccinated population, is not only a reflection of the high vaccination coverage in Vietnam but also portrays a possibility of less than 100% vaccine efficacy. More so, in order to prevent measles in adults, high-risk groups must be identified and catch-up for selected groups selected.Conclusions: This study therefore reinforces the need for continued improvement of surveillance system and to probe into the possible role of changes in age-distribution of cases if the effective control of measles is to be achieved.

In recent times, tsunamis and typhoons have threatened Japan's coastal lands with increased flooding and salinity. Using satellite data, we monitored the effect of increased salinity on vegetation health in the coastal area of southern Japan, which was affected by flooding following Typhoon 9918 in 1999. An index of plant activity called the Normalized Difference Vegetation Index (NDVI) was evaluated before and after the typhoon, and the change in NDVI was computed as a comparison measure. The results were then correlated with electric conductivity, which is a measure of soil salinity. A strong negative correlation was found between NDVI ratio and salt concentration (r = -0.7731, n = 50, p \ 0.0001), indicating that the reduced NDVI values were attributable to increased salinity from the flooding. These results not only provide useful insight into a rapid method of assessing large-scale flood impacts using satellite data, but also validate the monitoring of NDVI as an indicator of salinity damage to vegetation. To summarise, by understanding the changes in vegetation health following natural disasters such as flooding (as revealed by NDVI), we can potentially develop improved management strategies.

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