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Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania.

Hardy A, Mageni Z, Dongus S, Killeen G, Macklin MG, Majambare S, Ali A, Msellem M, Al-Mafazy AW, Smith M, Thomas C - Parasit Vectors (2015)

Bottom Line: 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.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.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK. ajh13@aber.ac.uk.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

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Related in: MedlinePlus

Fitted function plots for the independent landscape variables in the model for predicting 2011 wet and dry season malaria infection hotspots on Unguja, Zanzibar. B = bushland, C = cultivated, F = natural forest, M = mangrove, S = scrub, U = urban. See Table 2 for a description of other variables and units. The variables soil infiltration rate and health facility condition are not included due their negligible influence on the models (see Table 4).
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Fig6: Fitted function plots for the independent landscape variables in the model for predicting 2011 wet and dry season malaria infection hotspots on Unguja, Zanzibar. B = bushland, C = cultivated, F = natural forest, M = mangrove, S = scrub, U = urban. See Table 2 for a description of other variables and units. The variables soil infiltration rate and health facility condition are not included due their negligible influence on the models (see Table 4).

Mentions: The fitted functions for the predicting variables were similar for both wet and dry season models (Figure 6) although some small differences existed for the landcover variable. Specifically, areas of bushland were less associated with malaria infection hotspots in the dry season compared to the wet season. Conversely, cultivated land was more associated to malaria infection hotspots during the dry season, relative to the wet season. The scrubland landcover type had the greatest association with hotspots of malaria transmission. Geology had a similar level of influence in both the wet and dry season models. Of the different geological types, coralline and reef limestone (Q2) and the mixture of crystalline, reef and detrital limestone, with marine and fluvial sands and sandstone (Q2Q3M1) was shown to have the clearest relationship with hotspots of malaria transmission. The condition of the health facility had no influence on either the wet or dry season models meaning that subsequent inferences are independent from the state of facilities at which malaria incidence is reported.Figure 6


Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania.

Hardy A, Mageni Z, Dongus S, Killeen G, Macklin MG, Majambare S, Ali A, Msellem M, Al-Mafazy AW, Smith M, Thomas C - Parasit Vectors (2015)

Fitted function plots for the independent landscape variables in the model for predicting 2011 wet and dry season malaria infection hotspots on Unguja, Zanzibar. B = bushland, C = cultivated, F = natural forest, M = mangrove, S = scrub, U = urban. See Table 2 for a description of other variables and units. The variables soil infiltration rate and health facility condition are not included due their negligible influence on the models (see Table 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4307680&req=5

Fig6: Fitted function plots for the independent landscape variables in the model for predicting 2011 wet and dry season malaria infection hotspots on Unguja, Zanzibar. B = bushland, C = cultivated, F = natural forest, M = mangrove, S = scrub, U = urban. See Table 2 for a description of other variables and units. The variables soil infiltration rate and health facility condition are not included due their negligible influence on the models (see Table 4).
Mentions: The fitted functions for the predicting variables were similar for both wet and dry season models (Figure 6) although some small differences existed for the landcover variable. Specifically, areas of bushland were less associated with malaria infection hotspots in the dry season compared to the wet season. Conversely, cultivated land was more associated to malaria infection hotspots during the dry season, relative to the wet season. The scrubland landcover type had the greatest association with hotspots of malaria transmission. Geology had a similar level of influence in both the wet and dry season models. Of the different geological types, coralline and reef limestone (Q2) and the mixture of crystalline, reef and detrital limestone, with marine and fluvial sands and sandstone (Q2Q3M1) was shown to have the clearest relationship with hotspots of malaria transmission. The condition of the health facility had no influence on either the wet or dry season models meaning that subsequent inferences are independent from the state of facilities at which malaria incidence is reported.Figure 6

Bottom Line: 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.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.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK. ajh13@aber.ac.uk.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Show MeSH
Related in: MedlinePlus