Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania
| Autor: | Mwinyi I. Msellem, Mark W. Smith, Christopher Thomas, Zawadi D. Mageni, Stefan Dongus, Mark G. Macklin, Andy Hardy, Abdullah S. Ali, Silas Majambare, Gerry F. Killeen, Abdul-wahiyd H Al-mafazy |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Wet season
Water flow Rain Geographic Mapping Context (language use) Land cover Mosquito breeding habitat Tanzania wa_110 Hydrology (agriculture) Dry season parasitic diseases medicine Animals Humans Geomorphology Hydrology geography geography.geographical_feature_category Geography Research Geology wc_755 15. Life on land Karst medicine.disease Vector control wc_750 3. Good health Insect Vectors Malaria Infectious Diseases qx_510 Larva Regression Analysis Parasitology F810 Environmental Geography Seasons F840 Physical Geography Larval source management |
| Zdroj: | Parasites & Vectors |
| ISSN: | 1756-3305 |
| Popis: | Background\ud \ud Larval source management strategies can play an important role in malaria elimination programmes, especially for tackling outdoor biting species and for eliminating parasite and vector populations when they are most vulnerable during the dry season. Effective larval source management requires tools for identifying geographic foci of vector proliferation and malaria transmission where these efforts may be concentrated. Previous studies have relied on surface topographic wetness to indicate hydrological potential for vector breeding sites, but this is unsuitable for karst (limestone) landscapes such as Zanzibar where water flow, especially in the dry season, is subterranean and not controlled by surface topography.\ud \ud Methods\ud \ud We examine the relationship between dry and wet season spatial patterns of diagnostic positivity rates of malaria infection amongst patients reporting to health facilities on Unguja, Zanzibar, with the physical geography of the island, including land cover, elevation, slope angle, hydrology, geology and geomorphology in order to identify transmission hot spots using Boosted Regression Trees (BRT) analysis.\ud \ud Results\ud \ud The distribution of both wet and dry season malaria infection rates can be predicted using freely available static data, such as elevation and geology. Specifically, high infection rates in the central and southeast regions of the island coincide with outcrops of hard dense limestone which cause locally elevated water tables and the location of dolines (shallow depressions plugged with fine-grained material promoting the persistence of shallow water bodies).\ud \ud Conclusions\ud \ud This analysis provides a tractable tool for the identification of malaria hotspots which incorporates subterranean hydrology, which can be used to target larval source management strategies. |
| Databáze: | OpenAIRE |
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