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A hybrid seasonal prediction model for tuberculosis incidence in China.
BMC Med Inform Decis Mak. 2013 May 02; 13:56.BM

Abstract

BACKGROUND

Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China.

METHODS

Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model.

RESULTS

Although both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June.

CONCLUSIONS

The hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.

Authors+Show Affiliations

School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Case Reports
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23638635

Citation

Cao, Shiyi, et al. "A Hybrid Seasonal Prediction Model for Tuberculosis Incidence in China." BMC Medical Informatics and Decision Making, vol. 13, 2013, p. 56.
Cao S, Wang F, Tam W, et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak. 2013;13:56.
Cao, S., Wang, F., Tam, W., Tse, L. A., Kim, J. H., Liu, J., & Lu, Z. (2013). A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Medical Informatics and Decision Making, 13, 56. https://doi.org/10.1186/1472-6947-13-56
Cao S, et al. A Hybrid Seasonal Prediction Model for Tuberculosis Incidence in China. BMC Med Inform Decis Mak. 2013 May 2;13:56. PubMed PMID: 23638635.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A hybrid seasonal prediction model for tuberculosis incidence in China. AU - Cao,Shiyi, AU - Wang,Feng, AU - Tam,Wilson, AU - Tse,Lap Ah, AU - Kim,Jean Hee, AU - Liu,Junan, AU - Lu,Zuxun, Y1 - 2013/05/02/ PY - 2012/11/08/received PY - 2013/04/26/accepted PY - 2013/5/4/entrez PY - 2013/5/4/pubmed PY - 2013/7/17/medline SP - 56 EP - 56 JF - BMC medical informatics and decision making JO - BMC Med Inform Decis Mak VL - 13 N2 - BACKGROUND: Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China. METHODS: Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model. RESULTS: Although both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June. CONCLUSIONS: The hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries. SN - 1472-6947 UR - https://www.unboundmedicine.com/medline/citation/23638635/A_hybrid_seasonal_prediction_model_for_tuberculosis_incidence_in_China_ L2 - https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-13-56 DB - PRIME DP - Unbound Medicine ER -