What type of study is this?

This is a Time Series Analysis study.

September 10, 2025

Time Series Analysis on Malaria Disease and Contorl in Bauchi State

UHU. HarunaAbubakar Tafawa Balewa University AFAndrés Roca FLebanese University MAManal Abdul‐HamidBeni-Suef University

Key Points

The ARFIMA model shows a better fit for malaria trends compared to ARIMA and SARIMA models, indicating a clear trend in case analysis.
Time series data was collected monthly over 10 years to understand malaria patterns, with a focus on statistical modeling for forecasting.
Unit root tests confirmed data stationarity, allowing for reliable forecasting of malaria cases using advanced time series techniques.
The trend analysis indicates that malaria cases in Bauchi State are likely decreasing, suggesting effective interventions are in place.

Abstract

In many developing countries in Africa, the control of non-infectious diseases can be quite challenging due to a combination of factors such as poor housing, inadequate health care facilities, and poor sanitation. Nigeria has been recorded as the second highest country facing such challenges. Despite the measures that were taken by individuals, government and N.G.Os through vector control interventions with the use of insecticide-treated bed nets (ITN) and indoor residual spraying (IRS), but seasonal cases of Malaria couldn’t be eradicated completely. Furthermore, 120 samples (monthly) data were captured and analyzed for 10year using R-Statistical Software from the Bauchi State Agency for the control of HIV/AIDS, Tuberculosis, Leprosy and Malaria’s (BACATMA) register and different Time Series Model(TSM) were estimated and compared to explain the scenario of malaria cases in Bauchi. ARIMA, SARIMA, and ARFIMA models were fitted separately, model parameters were estimated and the model with the best fit was selected using AIC in each of the separate models, and model stationarity and independence were analyzed using the moving average method. Among these good models, an optimal model was chosen using both Likelihood test and AIC statistically. The ARFIMA(Phi) model performs better than ARIMA(1,0,1) and SARIMA(1,1,1). Unit root tests were conducted using ADF and PP tests. Results confirmed that the data series is stationary since the p-values 0.01 are less than alpha (0.05) for ADF test statistics and test statistics -5.3369 and -4.2434 which is less than critical-value (0.148) at 5% level of significance for PP test respectively and variables in the dataset were tested for normality using the Jargue-Bera test. The result showed that none of the variables is normally distributed as indicated by the p-values for all variables being less than selected level of significance for the study (0.05). The forecasting model suggests that the number of malaria cases in Bauchi state is decreasing probably to extinction. The model has used the moving average method and trend analysis for time series analysis to predict the future rate of malaria cases in the state.

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