GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi

Hassaan, Mahmoud A.; Abdrabo, Mohamed; Masabarakiza, Prosper

doi:10.4236/gep.2017.511008

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…In this study precipitation, slope, and LULC were related to malaria risk. Similar results have been reported in Chimoio [11], in Zimbabwe [22], Burundi [23], Zambia [27], Ghana [28], Colombia [28,29], and China [30].…”

Section: Discussionsupporting

confidence: 89%

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Ferrão¹,

Earland²,

Novela³

et al. 2021

Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

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Background: Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. (2) Methods: One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. (3) Results:The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. (4) Conclusion:In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions.

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Section: Discussionsupporting

confidence: 89%

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Ferrão¹,

Earland²,

Novela³

et al. 2021

Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

View full text Add to dashboard Cite

show abstract

“…Thereafter, each factor in the reciprocal matrix was divided by the sum of its column. Finally, the weight of each factor was calculated as an average across the rows [ 23 ]. The true consistency ratio was used after the pairwise matrix derivation, which was calculated by dividing the consistency index for the set judgments by the index for the corresponding random matrix.…”

Section: Methodsmentioning

confidence: 99%

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Ferrão

Earland

Novela³

et al. 2021

IJERPH

View full text Add to dashboard Cite

Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions.

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“…The case is also true for Africa, as the continent is viewed as the most vulnerable region to climate variability and change. Regionally in East Africa studies indicate that in countries like Burundi, Kenya, Sudan and Tanzania people are hit high by the impacts of climate change (Hassaan et al 2017;Mwangi and Mutua 2015;Shemsanga et al 2010). In Ethiopia, within the last decades, high temperature values recorded in different parts of the country.…”

Section: Introductionmentioning

confidence: 99%

GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa

Feyissa

Zeleke

Gebremariam

et al. 2018

Geoenviron Disasters

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Background: Unlike the studies undertaken on agricultural and hydrological sectors, focused climate change vulnerability researches in urban centers in Ethiopia is not widely available and of recent history. However, as many signals of climate change vulnerability started to happen in urban centers as well, it is inevitable to analyze, quantify, map, prioritize and be prepared for adaptation measures. This study is therefore, tried to assess, quantify and map climate change vulnerability in Addis Ababa, Ethiopia, by integrating two climate change vulnerability assessment models, namely, the Sullivan and Meigh's Model of composite climate change vulnerability index and the IPCC's approach of vulnerability assessment which comprises exposure, sensitivity and adaptive capacity. Fifteen subcomponents of vulnerability indicators were identified in ten sub-cities (Addis Ketema, Arada, Akaki-Kality, Bole, Gulelle, Kirkos, Kolfe-Keraniyo, Lideta, Nifasilk-Lafto and Yeka) of Addis Ababa. Due to the scale, degree, amount and unit of measurement for the selected indicators varied, their values were normalized to a number which ranges between 0 and 1, indicating as the values increased to 1, vulnerability to climate change increases. The study uses Iyengar and Sudarshan's unequal weighting system, to assign a weight to all indicators. The results were mapped using ArcGIS 10.2 package. Results: The results indicated that the ten sub-cities in Addis Ababa were found in different levels of vulnerability to climate change. The exposure and sensitivity were highest for Addis Ketema, Arada, and Lideta which are found in central parts of the city, with a normalization index value greater than 0.5. The adaptive capacity index is the highest in Gulelle, Bole, and Arada sub-cities. These sub-cities have better quality houses, well-planned districts, good infrastructural facilities and good coverage of green areas compared to others. The overall climate change vulnerability was the highest (normalized index > 0.5) in Arada, Addis Ketema and Lideta, due to the adaptation capacity is the lowest compared to other sub-cities. Conclusion: Addis Ababa is vulnerable to climate change impacts and the degree of vulnerability is underpinned by the interaction of multiple factors mainly adaptive capacities of sub-cities, location based characteristics and changes in climatic parameters. These present a need to strengthen mitigation and adaptation activities and prioritize sub cities for intervention based on the degree of vulnerability. It is also understood that the Sullivan and Meigh's Model and IPCC's approach for climate change analyses, could be used simultaneously for preparing vulnerability index per different geographical locations.

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GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi

Cited by 11 publications

References 18 publications

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

Mapping Risk of Malaria as a Function of Anthropic and Environmental Conditions in Sussundenga Village, Mozambique

GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa

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