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Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.
Int J Environ Res Public Health. 2016 07 26; 13(8)IJ

Abstract

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.

Authors+Show Affiliations

Biostatistics and Epidemiology Research Group, Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. azizadeboye@gmail.com. Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. azizadeboye@gmail.com.Biostatistics and Epidemiology Research Group, Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. daviesobaromi@gmail.com. Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. daviesobaromi@gmail.com.Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. aodeyemi@ufh.ac.za.Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. jndege@ufh.ac.za.Department of Statistics, University of Fort Hare, PMB X1314, Alice 5700, South Africa. mmutambayi@ufh.ac.za.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27472353

Citation

Azeez, Adeboye, et al. "Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model." International Journal of Environmental Research and Public Health, vol. 13, no. 8, 2016.
Azeez A, Obaromi D, Odeyemi A, et al. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model. Int J Environ Res Public Health. 2016;13(8).
Azeez, A., Obaromi, D., Odeyemi, A., Ndege, J., & Muntabayi, R. (2016). Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model. International Journal of Environmental Research and Public Health, 13(8). https://doi.org/10.3390/ijerph13080757
Azeez A, et al. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model. Int J Environ Res Public Health. 2016 07 26;13(8) PubMed PMID: 27472353.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model. AU - Azeez,Adeboye, AU - Obaromi,Davies, AU - Odeyemi,Akinwumi, AU - Ndege,James, AU - Muntabayi,Ruffin, Y1 - 2016/07/26/ PY - 2016/06/13/received PY - 2016/07/19/revised PY - 2016/07/20/accepted PY - 2016/7/30/entrez PY - 2016/7/30/pubmed PY - 2017/8/5/medline KW - autocorrelation KW - co-infection KW - neutral-network KW - non-seasonality KW - prediction JF - International journal of environmental research and public health JO - Int J Environ Res Public Health VL - 13 IS - 8 N2 - 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. SN - 1660-4601 UR - https://www.unboundmedicine.com/medline/citation/27472353/Seasonality_and_Trend_Forecasting_of_Tuberculosis_Prevalence_Data_in_Eastern_Cape_South_Africa_Using_a_Hybrid_Model_ L2 - http://www.mdpi.com/resolver?pii=ijerph13080757 DB - PRIME DP - Unbound Medicine ER -