Forecasting of the Number of Patients for Tetanus and Tetanus Neonatorum with Box-Jenkins Method
Abstract
The objective of this research is to forecast the number of patients for Tetanus and Tetanus Neonatorum with Box-Jenkins method using secondary data from the Bureau of Epidemiology, Department of Disease Control. The number of patients for Tetanus and Tetanus Neonatorum were collected since May, 2008 to April, 2018 to generate forecasting model then predict the number of patients for Tetanus and Tetanus Neonatorum from May, 2018 to April, 2019. The accuracy of forecasting was monitored by root mean squared error (RMSE), mean absolute error (MAE), normalized mean square error (NMSE) and factor of two (FA2). The research results revealed that 5 models from Box-Jenkins method with no constant are built; ARIMA(1,0,1)x(2,1,1)12, ARIMA(0,1,1)x(2,1,1)12, ARIMA(1,0,1)x(1,1,1)12, ARIMA(1,0,1)x(1,0,2)12 and ARIMA(0,1,1)x(1,0,2)12. The most appropriate model from Box-Jenkins method with no constant for forecasting the number of patients of Tetanus and Tetanus Neonatorum is ARIMA(1,0,1)x(2,1,1)12 with RMSE of 2.8997, MAE of 1.8734 and NMSE of 0.1282 and FA2 of 0.9630.
Keywords : Tetanus and Tetanus Neonatorum ; forecasting ; Box-Jenkins method
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Anderson, T. W., & Darling, D. A. (1954). A test of goodness of fit. Journal of the American statistical
association, 49(268), 765-769.
Bureau of Epidemiology, Department of disease control. (2019). National disease surveillance (report506).
Retrieved May 5, 2019, form: http://www.boe.moph.go.th/boedb/surdata/disease.php?ds=90. (in Thai)
Bowerman, BL., & O’Connell, RT. (1993). Forecasting and time series: An Applied Approach. 3rd ed.
California: Duxbury Press.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis forecasting and control. 2nd ed. San Francisco:
Holden-day.
Charoensiri, T., Soponpimol, C., & Thanapala, D. (2015). Forecasting the number of chicken pox in
Chiang mai with box-Jenkins method. Burapha journal of medicine, 2(2), 41-49. (in Thai)
Deesomchok, A. (2010). Tetanus neonatorum. Buddhachinaraj medical journal, 27(2), 233-242. (in Thai)
Keerativibool, W. (2016). Forecasting model for the number of patients with pneumonia in Thailand.
The public health journal of Burapha university, 11(1), 24-38. (in Thai)
Lambo, J. A., Memon, M. I., Khahro, Z. H., & Lashari, M. I. (2011). Epidemiology of Neonatal Tetanus in
Rural Pakistan. Journal of the Pakistan Medical Association, 61(11), 1099-1103.
Levene, H. (1960). Robust tests for equality of variances. In Ingram Olkin; Harold Hotelling; et al. (eds.).
Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press,
–292.
Ljung, G. M., & Box, G. E. (1979). The likelihood function of stationary autoregressive moving average models.
Biometrika, 66(2), 265-270.
Mekparyup, J., Khamrod, S., & Saithanu, K. (2015). Estimation of Malaria cases with MLR method in Prajuab
Kirikhan, Thailand. Global Journal of Pure and Applied Mathematics, 11(1), 519-522.
Mekparyup, J., Kornpetpanee, S., & Saithanu, K. (2015). Forecasting the Dengue Hemorrhagic Fever cases
using seasonal ARIMA model in Chonburi, Thailand. Global Journal of Pure and Applied Mathematics,
(1), 401-407.
Mekparyup, J., & Saithanu, K. (2015a). Hybrid methods to forecast epidemic of Dengue Hemorrhagic Fever.
Global Journal of Pure and Applied Mathematics, 11(4), 2155-2160.
Mekparyup, J., & Saithanu, K. (2015b). Combining seasonal ARIMA model and adjusted Tukey's control chart
with interpretation rules for monitoring epidemic of Dengue Hemorrhagic Fever. Global Journal of Pure
and Applied Mathematics, 11(4), 2151-2154.
Mekparyup, J., & Saithanu, K. (2015c). Monitoring epidemic of DHF using time series model and robustness
control chart. Global Journal of Pure and Applied Mathematics, 11(4), 2145-2149.
Mekparyup, J., & Saithanu, K. (2015d). An approach model to forecast the number of DHF cases in
Chanthaburi Province, Thailand. Global Journal of Pure and Applied Mathematics, 11(3), 341-347.
Mekparyup, J., & Saithanu, K. (2015e). Modeling and forecasting the Dengue Hemorrhagic Fever cases in
Trat, Thailand. Global Journal of Pure and Applied Mathematics, 11(2), 837-841.
Mekparyup, J., & Saithanu, K. (2015f). A seasonal ARIMA model for forecasting the dengue hemorrhagic fever
patients in Rayong, Thailand. Global Journal of Pure and Applied Mathematics,11(2), 175-181.
Mekparyup, J., & Saithanu, K. (2016). A new approach to detect epidemic of DHF combining ARIMA model
and adjusted Tukey’s control chart with interpretation rules. Interventional Medicine and Applied
Science, 8(3), 118-120.
Mekparyup, J., Saithanu, K., & Treewong, N. (2015). Analysis of effect of meteorological factors on the number
of Dengue Fever patients with multiple linear regression. Global Journal of Pure and Applied
Mathematics, 11(3), 1393-1397.
Pothisamutyothin, K. (2015). Vaccine prevention of tetanus and tetanus neonatorum. R&D newsletter 2015,
(1): 18-19. (in Thai)
Samjai, P. (2014). Tetanus and tetanus neonatorum. Annual epidemiological surveillance report 2014, 52-53.
(in Thai)
Samjai, P. (2015). Tetanus and tetanus neonatorum. Annual epidemiological surveillance report 2015, 56-57.
(in Thai)
Saritsiri, S., & Katanyoothita, S. (2012). Case investigation of tetanus death in May 2011, Taweewattana district,
Bangkok. Weekly epidemiological surveillance report, Thailand, 43(12), 177-183. (in Thai)
Siriudompak, S. (2019). Tetanus. Retrieved March 15, 2019, form http://www.haamor.com/th/บาดทะยัก. (in Thai)
Songkasri, C. (2012). Model forecasting of dengue haemorrhagic fever diseases in office of diseases
prevention and control 6 Khonkaen province, 2012. Journal of the office of DPC 7 Khon kaen, 20(1),
-81. (in Thai)
Teerapiboon, P., Tanphaichitr, A., & Sununliganon, L. (2008). Tetanus in adult diseases that still need to be
thought. Journal of Thammasat university medical school, 8(3), 402-408. (in Thai)
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