Time series analysis of malaria fever prevalence in Ogun State

Abstract

Malaria fever remains the most important parasitic disease in terms of its public health implications. This research study investigated the trend and seasonality of malaria fever in Ogun State, Nigeria over the period 2016-2021 and also predict its prevalence in the year 2023. The study used secondary data obtained from the World Development Indicators. The data was pre-processed to ensure that it was in the appropriate format for time series analysis. The time series data was decomposed into its trend, seasonality, and residual components using the seasonal decomposition of time series (STL) method, ARIMA (Autoregressive Integrated Moving Average) modeling was used to describe the general behavior and pattern of occurrence of the malaria fever over the period under study and forecasts of future occurrence. It was revealed that relatively large number of infected patients during the study period are seasonal in nature. The results of this study predicted that there will be a 50% reduction in the trends of malaria fever in Ogun state for the year 2023 provided there are appropriate preventive and control measures in places. However, the study recommends that the Government and Malaria Control Agencies should provide more preventive measures and effective intervention strategies such as vaccines, mosquito nets, insecticides to control malaria fever in Ogun State.