A time series analysis of TB cases for the period 2008-2018

Abstract

In Uganda like the rest of the world, the interaction between TB and HIV has not only increased the burden of both diseases amongst our people but has also led to an urgent need to control the transmission of TB and HIV. The emergence of Multi drug resistant TB complicates the picture further. This investigates the times series cases of TB in Uganda between 2008 to 2018 and the frequency of cases was in months with the objective to see the trend and forecast of cases. The study used the ARIMA model to achieve its Objectives. The analysis used approach of Box–Jenkins’s methodology, where modelling including both finite and infinite lag models to forecast TB incident cases in the Country. A time series plots of the TB cases depicted that the series fluctuates with time in an increasing and decreasing. The Autocorrelation and Partial Autocorrelation plots, the proposed candidate models that were tested to be fitted due to TB include ARIMA (2,1,0). The models developed for predicting the monthly TB cases was adequate for representing the series as evident from all the diagnostics and model comparison techniques employed in the study. However, the forecast, was based on assessment from the linear ARIMA model, Predicted TB cases. The trend model depicts a decreasing level in the number of pneumonia cases for a unit change in time. This decreasing level does not warrant public health workers to suggest that pneumonia cases are not prevalent in the region. It is rather recommended that the MoH should collaborate with health personnel to provide intensive education on some of the dangers of the disease and the need to seek early treatment in any nearby health facility because there can be a reverse trend of the cases as in the case of the any outbreak of TB in the country. The Study also recommended that the MoH and local health centers advise the heads of its various institutions in the country to make data on pneumonia cases available. This will make it possible for researchers to study and predict pneumonia cases ahead of time for policy formulation and implementation to advert future case of the TB in the country