Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression

Detalhes bibliográficos
Autor(a) principal: Dadi, Berhanu Berga
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/95138
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regressionEthiopiaMalaria casesNon-stationarySpatial heterogeneityRisk factorsGeographically weighted regressionDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesIn Ethiopia, still, malaria is killing and affecting a lot of people of any age group somewhere in the country at any time. However, due to limited research, little is known about the spatial patterns and correlated risk factors on the wards scale. In this research, we explored spatial patterns and evaluated related potential environmental risk factors in the distribution of malaria cases in Ethiopia in 2015 and 2016. Hot Spot Analysis (Getis-Ord Gi* statistic) was used to assess the clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semiparametric geographically weighted regression (s-GWR) models were compared to describe the spatial association of potential environmental risk factors with malaria cases. Our results revealed a heterogeneous and highly clustered distribution of malaria cases in Ethiopia during the study period. The s-GWR model best explained the spatial correlation of potential risk factors with malaria cases and was used to produce predictive maps. The GWR model revealed that the relationship between malaria cases and elevation, temperature, precipitation, relative humidity, and normalized difference vegetation index (NDVI) varied significantly among the wards. During the study period, the s-GWR model provided a similar conclusion, except in the case of NDVI in 2015, and elevation and temperature in 2016, which were found to have a global relationship with malaria cases. Hence, precipitation and relative humidity exhibited a varying relationship with malaria cases among the wards in both years. This finding could be used in the formulation and execution of evidence-based malaria control and management program to allocate scare resources locally at the wards level. Moreover, these study results provide a scientific basis for malaria researchers in the country.Mateu Mahiques, JorgeCosta, Ana Cristina Marinho daVerdoy, Pablo JuanRUNDadi, Berhanu Berga2020-03-27T08:29:50Z2020-03-052020-03-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/95138TID:202464989enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:43:09Zoai:run.unl.pt:10362/95138Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:14.240835Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
title Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
spellingShingle Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
Dadi, Berhanu Berga
Ethiopia
Malaria cases
Non-stationary
Spatial heterogeneity
Risk factors
Geographically weighted regression
title_short Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
title_full Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
title_fullStr Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
title_full_unstemmed Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
title_sort Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
author Dadi, Berhanu Berga
author_facet Dadi, Berhanu Berga
author_role author
dc.contributor.none.fl_str_mv Mateu Mahiques, Jorge
Costa, Ana Cristina Marinho da
Verdoy, Pablo Juan
RUN
dc.contributor.author.fl_str_mv Dadi, Berhanu Berga
dc.subject.por.fl_str_mv Ethiopia
Malaria cases
Non-stationary
Spatial heterogeneity
Risk factors
Geographically weighted regression
topic Ethiopia
Malaria cases
Non-stationary
Spatial heterogeneity
Risk factors
Geographically weighted regression
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-27T08:29:50Z
2020-03-05
2020-03-05T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/95138
TID:202464989
url http://hdl.handle.net/10362/95138
identifier_str_mv TID:202464989
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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