Background: Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. Methods: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Results: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. Conclusions: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

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Aim This is an applied study to investigate the association of selected socio-economic and demographic factors with the relative risk of tuberculosis (TB) prevalence in the Eastern Cape Province of South Africa and to produce disease maps for the spatial outlines of the disease in the province. Subjects and methods This is an ecological spatial study of TB prevalence in the Eastern Cape, a province in South Africa, during the year 2014. Three socio-economic indicators and three demographic factors, all calculated per sub-district, were used to assess their relationship with tuberculosis prevalence, using a Poisson regression model. Results From the analysis, the best model included all the selected covariates of the proximal model with the spatial random effects. The improvement in the goodness-of-fit statistic when the spatial structure was included confirms the spatial pattern of population density and average household size. Conclusion The idea of assessing both the impact of covariates at the ecological level and spatial outlines in the same context should be encouraged in epidemiology to help with creating epidemiological surveillance systems (ESS) on a provincial basis for planning interventions and improvement of control programme efficiency.

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