Time Series Analysis of Malaria Cases in Kasena Nankana Municipality (original) (raw)

2017, International journal of statistics and applications

According to National Institute of Allergy and Infectious Diseases (2007), Malaria is a disease caused by a parasite that lives part of its life in humans and part in mosquitoes. Malaria remains one of the major killers of humans worldwide, threatening the lives of more than one-third of the world's population. It thrives in the tropical areas of Asia, Africa, and Central and South America, where it strikes millions of people. Sadly, more than 1 million of its victims, mostly young children, die yearly. A study from the Navrongo Health Research Centre in the Kasena Nankana Municipal discovered that, in this physical Ghanaian rural setting, and highly endemic area of malaria, if malaria were eliminated as a cause of death, persons could live at least 6.1 years longer. This research was therefore undertaken with the prior motive to develop an adequate model for forecasting future trends of malaria in the Kasena Nankana Municipality. The analysis preliminarily evolved that, the reported cases of malaria in the municipality is higher from the months June to November. These months happen to be the major rainy seasons with the highest humidity in the study area and the country as whole. Descriptively, the study revealed that, the average number of patients diagnosed with malaria is 698.7 having slightly flat tail at right side (positively skewed) which implies that the malaria cases are heading towards more positive values with the value of being less than 3 hence making them not normally distributed (platykurtic) which means the variables exhibit broad peaks high kurtosis. However, values with high skewness and kurtosis turns to produce inadequate result but in this case, a small value such as what was obtained in the descriptive statistics of figure 4.1 shows that the model ARIMA (1, 0, 1) is Also, with the help of the ACF and PACF plots, tentative models were fit to the data. ARIMA (1, 0, 1) was noted to fit the data well. Further adequacy test on the model also confirmed the validity of the selected model. The model was used to forecast for monthly cases of malaria for the next two years.