Mapping Malaria Prevalence Using Geographic Information Systems (GIS) in Rivers State, Nigeria

Egbom, S. E.; Nduka, Florence O.; Nzeako, Sidney O.

doi:10.9734/ijtdh/2022/v43i1030617

Cited by 2 publications

(3 citation statements)

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“…× 100 (10) where n is the number of observations, 𝑦 𝑖 is the actual value for the i-th month, 𝑦 ̂𝑖 is the predicted value for the i-th month.…”

Section: Mean Absolute Percentage Error (Mape)mentioning

confidence: 99%

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Exploring the Interplay of Meteorological Variables, Government Policies, and Malaria Epidemiology: Towards an Optimal Predictive Model for Malaria Spread in Rivers State, Nigeria

Egbom,

Nduka,

Nzeako

et al. 2024

Preprint

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Background: Malaria continues to be a serious public health issue, especially in tropical and subtropical areas where the dynamics of its transmission are greatly influenced by environmental conditions. The objective of this research is to examine the impact of meteorological factors, and governmental policies on malaria epidemiology to determine the most effective model for forecasting and comprehending the transmission of malaria in Rivers State, Nigeria. Method: Three statistical models for count data were compared to identify the most influential meteorological variables and government policy and establish their associations with malaria transmission. The best count data model was compared with a seasonal autoregressive integrated moving average model using some known model evaluation metrics. Results: The results obtained showed that the best count data model out of the two models considered in this study is the Quasi-Poisson Model because it resulted in a smaller Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) value. The SARIMAX Model outperformed the Quasi-Poisson model and showed that season, precipitation and government policies were significant at a 5% level of significance in explaining the variations in the monthly number of malaria cases in Rivers State, Nigeria (Jan. 2007 – Dec. 2021). Further, the SARIMAX (1,1,0)(1,1,1)12 model showed that the current number of malaria cases depends on the past year’s number of cases. 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 governance-related determinant 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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“…× 100 (10) where n is the number of observations, 𝑦 𝑖 is the actual value for the i-th month, 𝑦 ̂𝑖 is the predicted value for the i-th month.…”

Section: Mean Absolute Percentage Error (Mape)mentioning

confidence: 99%

“…Rivers State, in Nigeria's Niger Delta region, is one of the states having a high incidence of malaria cases. Malaria remains endemic in Rivers State [6], [7], [8], [9], [10], [11] with spatiotemporal variations reported [12]. It is a major source of illness and death in Rivers State, despite the availability of efficacious interventions.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Interplay of Meteorological Variables, Government Policies, and Malaria Epidemiology: Towards an Optimal Predictive Model for Malaria Spread in Rivers State, Nigeria

Egbom,

Nduka,

Nzeako

et al. 2024

Preprint

Self Cite

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Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Meteorological Parameters on Malaria Epidemiology: Identification of an Optimal Model for Application

Egbom

Nduka

Nzeako

et al. 2023

Preprint

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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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Mapping Malaria Prevalence Using Geographic Information Systems (GIS) in Rivers State, Nigeria

Cited by 2 publications

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Exploring the Interplay of Meteorological Variables, Government Policies, and Malaria Epidemiology: Towards an Optimal Predictive Model for Malaria Spread in Rivers State, Nigeria

Exploring the Interplay of Meteorological Variables, Government Policies, and Malaria Epidemiology: Towards an Optimal Predictive Model for Malaria Spread in Rivers State, Nigeria

Modeling the Effect of Meteorological Parameters on Malaria Epidemiology: Identification of an Optimal Model for Application

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