Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression
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Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regressionAutoria
Tutor/Supervisor; Universitat.Departament
Mateu Mahiques, Jorge; Universitat Jaume I. Departament de MatemàtiquesData de publicació
2020-03Editor
Universitat Jaume IResum
In 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 ... [+]
In 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. [-]
Paraules clau / Matèries
Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | Erasmus Mundus University Master's Degree in Geospatial Technologies | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | Ethiopia | geographically weighted regression | Malaria cases | nonstationary | spatial heterogeneity | risk factors
Descripció
Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2019-2020
Tipus de document
info:eu-repo/semantics/masterThesisDrets d'accés
info:eu-repo/semantics/openAccess
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