SPATIAL DISTRIBUTION OF MALARIA AND LAND COVER PATTERNS IN OGONI LAND, RIVERS STATE, NIGERIA - Egbom, Sylvia; Nduka, Florence Onyemachi; Nzeako, Sidney Obidimma; Ikechukwu, Chigozie C.; Joseph I. Nwachukwu; Chinonye O. Ezenwaka; Charity O. Ihejirika; Martin C. Opara; Emilia O. Anyanwu; Blessing O. Agoh; Justin N. Okorondu; Grace C. Egwuogu (Malaysian Journal of Science, 2023)
The distribution of malaria is characterised by microgeographic variations determined by a range of factors, including the local environment. A study on the spatial distribution of malaria about land cover patterns was carried out by sampling Primary Health Centres in Ogoni Land. Nine Primary Healthcare Centres (PHCs) were selected across four local government areas (LGA) using Systematic Grid Point Sampling. Human blood samples were obtained from 318 consented individuals, and questionnaires were administered to obtain demographic data. Plasmodium species were identified through microscopy using thick and thin blood films. A geodatabase was created and imported into ArcGIS 10.7 to produce a thematic map of the study area. A cloud-free Landsat-8 Operational Land Imager (OLI) was employed for land cover analysis. Both supervised and unsupervised classifications of land cover were performed to generate the land cover classes. Pearson correlation was carried out to determine the significance between malaria distribution and land cover. Of the 318 individuals, 169 were infected with an overall prevalence of 53.1%.Only P. falciparum was identified and malaria distribution showed spatial variations. Across the PHCs sampled, the highest point prevalence was recorded in Model Primary Health Centre Koroma in Tai LGA whereas the lowest was recorded in MPHC Okwale in Khana LGA. Cumulatively, Kwawa PHC recorded the highest malaria prevalence whereas MPHC Bunu in Tai recorded the lowest prevalence. The highest prevalence was recorded in Khana LGA while the lowest was recorded in Eleme LGA. Land cover analysis revealed that Ogoni Land has a total land cover mass of 982.97km.2 Sparse vegetation dominated the study area (471.06km2) while dense vegetation covers a total mass of 213.1km2. Bivariate analysis showed a significant correlation between malaria prevalence and dense vegetation (p<0.05, 0.952). Dense vegetation played a significant role in malaria transmission in Ogoni Land. The study concludes that the presence of dense vegetation is associated with high malaria prevalence in the study area.