Your search found 3 records
1 Senanayake, F. R.; Soule, M.; Senner, J. W. 1977. Habitat values and endemicity in the vanishing rain forests of Sri Lanka. Nature, 265:351-354.
Habitats ; Rain forests ; Endemics ; Monsoon climate ; Rain ; Biogeographic regions ; Birds / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H045082)
(0.36 MB)
2 Kibret, S.; Lautze, Jonathan; McCartney, Matthew; Nhamo, Luxon; Yan, G. 2019. Malaria around large dams in Africa: effect of environmental and transmission endemicity factors. Malaria Journal, 18:1-12. [doi: https://doi.org/10.1186/s12936-019-2933-5]
Malaria ; Vector-borne diseases ; Dams ; Environmental effects ; Disease transmission ; Endemics ; Mosquitoes ; Anopheles ; Breeding habitats ; Water reservoirs ; Slope ; Topography ; Climatic data ; Communities ; Health hazards / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H049330)
https://malariajournal.biomedcentral.com/track/pdf/10.1186/s12936-019-2933-5
https://vlibrary.iwmi.org/pdf/H049330.pdf
https://vlibrary.iwmi.org/pdf/H049330.pdf
(3.62 MB) (3.62 MB)
Background: The impact of large dams on malaria has received widespread attention. However, understanding how dam topography and transmission endemicity influence malaria incidences is limited.
Methods: Data from the European Commission’s Joint Research Center and Shuttle Radar Topography Mission were used to determine reservoir perimeters and shoreline slope of African dams. Georeferenced data from the Malaria Atlas Project (MAP) were used to estimate malaria incidence rates in communities near reservoir shorelines. Population data from the WorldPop database were used to estimate the population at risk of malaria around dams in stable and unstable areas.
Results: The data showed that people living near (< 5 km) large dams in sub-Saharan Africa grew from 14.4 million in 2000 to 18.7 million in 2015. Overall, across sub-Saharan Africa between 0.7 and 1.6 million malaria cases per year are attributable to large dams. Whilst annual malaria incidence declined markedly in both stable and unstable areas between 2000 and 2015, the malaria impact of dams appeared to increase in unstable areas, but decreased in stable areas. Shoreline slope was found to be the most important malaria risk factor in dam-affected geographies, explaining 41–82% (P < 0.001) of the variation in malaria incidence around reservoirs.
Conclusion: Gentler, more gradual shoreline slopes were associated with much greater malaria risk. Dam-related environmental variables such as dam topography and shoreline slopes are an important factor that should be considered in efforts to predict and control malaria around dams.
Methods: Data from the European Commission’s Joint Research Center and Shuttle Radar Topography Mission were used to determine reservoir perimeters and shoreline slope of African dams. Georeferenced data from the Malaria Atlas Project (MAP) were used to estimate malaria incidence rates in communities near reservoir shorelines. Population data from the WorldPop database were used to estimate the population at risk of malaria around dams in stable and unstable areas.
Results: The data showed that people living near (< 5 km) large dams in sub-Saharan Africa grew from 14.4 million in 2000 to 18.7 million in 2015. Overall, across sub-Saharan Africa between 0.7 and 1.6 million malaria cases per year are attributable to large dams. Whilst annual malaria incidence declined markedly in both stable and unstable areas between 2000 and 2015, the malaria impact of dams appeared to increase in unstable areas, but decreased in stable areas. Shoreline slope was found to be the most important malaria risk factor in dam-affected geographies, explaining 41–82% (P < 0.001) of the variation in malaria incidence around reservoirs.
Conclusion: Gentler, more gradual shoreline slopes were associated with much greater malaria risk. Dam-related environmental variables such as dam topography and shoreline slopes are an important factor that should be considered in efforts to predict and control malaria around dams.
3 Mahendran, R.; Pathirana, S.; Piyatilake, I. T. S.; Perera, S. S. N.; Weerasinghe, M. C. 2020. Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: the wavelet approach. PLoS ONE, 15(2):e0228540. [doi: https://doi.org/10.1371/journal.pone.0228540]
Malaria ; Disease transmission ; Endemics ; Environmental factors ; Rural areas ; Arid zones ; Epidemiology ; Rivers ; Rain ; Humidity ; Models / Sri Lanka / Kataragama / Menik Ganga
(Location: IWMI HQ Call no: e-copy only Record No: H049856)
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0228540&type=printable
https://vlibrary.iwmi.org/pdf/H049856.pdf
https://vlibrary.iwmi.org/pdf/H049856.pdf
(3.19 MB) (3.19 MB)
Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the “Menik Ganga” river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the Department of Irrigation, Department of Meteorology and Malaria Research Unit (MRU) of University of Colombo, respectively, for the period 1990 to 2005. Wavelet theory was applied to analyze these monthly time series data. There were two significant periodicities in malaria cases during the period of 1992–1995 and 1999–2000. The cross-wavelet power spectrums revealed an anti-phase correlation of malaria cases with mean temperature, minimum temperature, and water flow of “Menik Ganga” river during the period 1991–1995, while the in-phase correlation with rainfall is noticeable only during 1991–1992. Relative humidity was similarly associated with malaria cases between 1991–1992. It appears that environmental variables have contributed to a higher incidence of malaria cases in Kataragama in different time periods between 1990 and 2005.
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