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In this study, change detection in Out-patient and In-patient malaria cases in the Northern Region of Ghana was examined using time series intervention analysis. Data on monthly Out-patient and In-patient malaria cases obtained from the Northern Regional Health Directorate were modelled using Seasonal Autoregressive Integrated Moving Average with an Independent variable (SARIMAX) and Autoregressive Integrated Moving Average with an Independent variable (ARIMAX) models. The results revealed that SARIMAX (1, 1, 1)(1, 1, 1)12 was the best model for predicting Out-patient malaria cases while SARIMAX (1, 1, 1)(2, 1, 1)12 emerged as the best model for predicting the In-patient cases in the region. Diagnostic checks of the two models with the Ljung-Box test and Autoregressive Conditional Heteroscedasticity Lagrange Multiplier (ARCH-LM) test revealed that both models were free from higher-order serial correlation and conditional heteroscedasticity respectively. A chi-square goodness-of-fit test also revealed that there was no significant difference between the predicted values from the models and the observed values for the year 2018. The study further revealed that the coefficients of the intervention variable for the Out-patient and In-patient cases were both negative, which suggest that the intervention policy the government of Ghana implemented brought about a decline in the number of Out-patient and In-patient cases in the region.
[Accessed on 23 February 2018]
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