Background: Nigeria aspires to eradicate malaria, and the significance of mapping in this endeavour has grown. The prevalence and spatial distribution of malaria in Rivers State were studied using data from Primary Healthcare Centres (PHCs). Methodology: PHCs in Rivers State were geo-referenced using the global positioning system (GPS) and 74 were selected across 21 local government areas using Systematic GridPoint Sampling Technique. Blood samples were obtained from 2340 consented individuals whose demographic data were obtained with structured questionnaires. Blood films were examined for Plasmodium spp. using standard parasitological techniques. An overall prevalence of 56.3% was recorded identifying only P.falciparum. Coordinates of PHCs sampled and the prevalence data for malaria were inputted into a spread sheet and imported into ArcGIS 10.7. This was used to generate prevalence maps of malaria infection in the State. Results: Ogu-Bolo, Omumma, Abua-Odual LGAs recorded very high LGA prevalence whereas Ikwerre, Abua-Odual, Ahoada West, Oyigbo and Ogba/Egbema/Ndoni LGAs recorded very high state-level prevalence. Eleme and Port Harcourt City LGAs had the least prevalence. Conclusion: The observed spatial variation could be attributed to land use land cover (LULC) patterns and further research to evaluate the impact of LULC patterns on the spatial distribution of malaria is recommended. This study provides malaria maps which will serve as a valuable resource to policy makers for targeted interventions in the State.
Background: This study aims to investigate the relationship between meteorological parameters and malaria epidemiology to identify an optimal model for predicting and understanding the spread of malaria in Rivers State of Nigeria. Malaria remains a significant public health concern, particularly in tropical and subtropical regions, where climatic factors play a crucial role in its transmission dynamics. By analyzing historical malaria and meteorological data from Rivers State, we developed a comprehensive modeling framework to quantify the impact of meteorological parameters on malaria incidence. Method: Five statistical models for count data were employed to identify the most influential meteorological variables and establish their associations with malaria transmission. Results: The results obtained show that, the best count data model out of the five models considered in this study is the Quasi-Poisson Regression Model because it resulted to smaller Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The Quasi-Poisson Regression Model showed that none of the meteorological variables used in the models were significant at 5% level of significance in predicting the number of cases of malaria in the study location. Conclusion: The findings of this study highlight the need for a multifaceted approach to malaria control in Rivers State, addressing not only the meteorological factors but also the biological, social and economic determinants of the disease. The identified optimal model serves as a valuable resource for policymakers, researchers, and healthcare practitioners, enabling them to make informed decisions and implement targeted interventions to mitigate the impact of malaria outbreaks.
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