Prediction case notification rates for tuberculosis in eight countries

Seng Hansun

Abstract

This article describes a novel approach to predicting tuberculosis case notification rates based on historical data for the eight countries with the highest tuberculosis rates. Using a relatively new prediction method known as Brown’s Weighted Exponential Moving Average, we produced excellent prediction results for the selected countries. In one example, we describe the implementation of the proposed tuberculosis case notification rates prediction model in China, which led to the most accurate prediction result in the study. The effectiveness of this new method was confirmed by our calculation of the prediction error using the mean absolute percentage error, showing that China had the lowest mean absolute percentage error value at 2.3606444%. New research results confirm that there is an increasing trend in tuberculosis case notification rates for most countries included in this research. These results can be used to support the decision-making process for all related stakeholders, including the governments of these countries, when managing the spread of tuberculosis.

 

 

Keywords: Brown’s weighted exponential moving average, case notification rate, high burden countries, prediction, tuberculosis.


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References


WORLD HEALTH ORGANIZATION. Global Tuberculosis Report 2019. Geneva, 2019. https://www.who.int/tb/publications/global_report/en/

DEPARTMENT OF HEALTH & HUMAN SERVICES. Tuberculosis - The Facts. https://www2.health.vic.gov.au/Api/downloadmedia/%7BB9E6FB0F-D3B6-47C2-A163-6E150A5A628C%7D

KURNIAWATI A., PADMAWATI R. S., and MAHENDRADHATA Y. Acceptability of mandatory tuberculosis notification among private practitioners in Yogyakarta, Indonesia. BMC Research Notes, 2019, 12(1): 543. https://doi.org/10.1186/s13104-019-4581-9

DEPARTMENT OF HEALTH & HUMAN SERVICES. Management, Control and Prevention of Tuberculosis: Guidelines for Health Care Providers, 2016. https://www2.health.vic.gov.au/about/publications/policiesandguidelines/tuberculosis-guidelines-2015

UPLEKAR M., ATRE S., WELLS W. A. et al. Mandatory tuberculosis case notification in high tuberculosis-incidence countries: policy and practice. European Respiratory Journal, 2016, 48: 1571–1581. https://doi.org/10.1183/13993003.00956-2016

SHELDON C.D., KING K., COCK H. et al. Notification of tuberculosis: how many cases are never reported?. Thorax, 1992, 47(12): 1015–1018. http://dx.doi.org/ 10.1136/thx.47.12.1015

HALIM S., INTAN R., and DEWI L.P. Fuzzy linear regression for tuberculosis case notification rate prediction in Surabaya. Proceedings of the International Conference on Advanced Information Science and System, New York, 2019, pp. 1–5. https://doi.org/10.1145/3373477.3373492

LI Z., WANG Z., SONG H. et al. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infection and Drug Resistance, 2019, 12: 1011–1020. https://doi.org/10.2147/IDR.S190418

HANSUN S. A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis. Balkan Journal of Electrical and Computer Engineering, 2016, 4(2): 75–78. https://doi.org/10.17694/bajece.14351

HANSUN S. WEMA versus B-WEMA Methods in Forex Forecasting. Proceedings of the 9th International Conference on Machine Learning and Computing, New York, 2017, pp. 268–271. https://doi.org/10.1145/3055635.3056565

ABDULLAH N.H., JUNAIDI, and HANDAYANI L. Peramalan Rate of Return Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt. Natural Sciences: Journal of Science and Technology, 2019, 8(3): 171–176. https://doi.org/10.22487/25411969.2019.v8.i3.14955

MUKHLASHIN P.A.R. and NUGRAHA J. Brown’s Weighted Exponential Moving Average (B-WEMA) with Levenberg-Marquardt Optimization to Forecasting Rate of Return. The Turkish Online Journal of Design, Art and Communication, 2018, 8: 1744–1749. https://doi.org/10.7456/1080SSE/232

HANSUN S. and KRISTANDA M.B. AQI Measurement and Prediction using B-WEMA Method. The International Journal of Engineering Research and Technology, 2019, 12(10): 1621–1625. http://irphouse.com/ijert19/ijertv12n10_02.pdf

HANSUN S. and KRISTANDA M.B. Forecasting Foreign Tourist Arrivals to Bali: Hybrid Double Exponential Smoothing Approach. The International Journal of Engineering Research and Technology, 2019, 12(11): 1864–1868. http://irphouse.com/ijert19/ijertv12n11_05.pdf

WORLD HEALTH ORGANIZATION. WHO’s Global Tuberculosis Database. https://www.who.int/tb/country/data/download/en/

HYNDMAN R.J. and KOEHLER A.B. Another look at measures of forecast accuracy. International Journal of Forecasting., 2006, 22: 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001

CHATTERJI M., MARKS G., LIAW S.-T., and VAN DRIEL M. Challenges in latent TB screening and treatment in primary care: A proposal to improve latent TB screening and treatment in Australia. Communicable Diseases Control Conference, Canmberra, 2019, pp. 105-108.


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