Rivers State is the centre of Nigeria's oil industry and has the presence of oil prospectors including expatriates who are at risk of malaria infection.  Periodic analysis of epidemiological data will enable malaria control programmers to appraise the interventions carried out over the years and assist in  the development of sustainable and adaptive strategies directed from an informed local level. This study, therefore, examined spatiotemporal variations  in malaria incidence in the State using Annual Parasite Incidence (API) as an indicator. Monthly reported malaria cases from 2007-2017 at the local  government area (LGA) level were retrieved from the Integrated Disease Surveillance Response (IDSR) system of Rivers State Ministry of Health while  projected population data for the same period were obtained from the National Bureau of Statistics. API of the LGAs from 2007 to 2017 were computed,  integrated into GIS, and subjected to weighted overlay analysis to delineate the risk zones. The eleven-year retrospective study of malaria in Rivers State  displayed geographical variations which were statistically significant between the LGAs. Malaria incidence fluctuated throughout the study period. API  values increased from 13.746 in 2007 to 34.067 in 2013 and dropped to 8.721 in 2017. All the LGAs recorded API values below 100, indicating a very low  malaria burden in a controlled setting. However, none of the LGAs has reached the WHO standard level for the elimination of transmission. Ikwerre,  Eleme, Ogu-Bolo and Opobo/Nkoro LGAs were assigned to the very high malaria risk stratum (362.615 to 490.005) whereas Abua-Odual, Akuku-Toru and  Degema LGAs were assigned to very low-risk malaria stratum (103.281 to 113.897). The findings of this research will aid stakeholders in evaluating the  impact of control strategies employed over the years and possibly, revisit malaria extant interventions for improved malaria control outcomes.

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.