Dengue Forecasting Model Using SARIMA Model to Predict the Incidence of Dengue in Thailand
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Published:
Jul 9, 2018
Keywords:
dengue incidence, SARIMA, forecasting model
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Abstract
Dengue is one of the major public health problems in the tropical countries of the world. SARIMA model is a popular method used for forecasting dengue incidence. The aim of this study was to determine optimal model for forecasting the dengue incidence. SARIMA model with Box-Jenkins approach was conducted to forecast dengue incidence using the previous data from 2006 to 2015. Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC) and Root Mean Square Error (RMSE) were used to determine their accuracy. The results showed that SARIMA (6, 0, 3) (0, 1, 1)52 were the best model that fitted with the actual data. It had the smallest AIC and BIC (3827.60 and 3873.30, respectively) and RMSE (0.8420).
Keywords: dengue incidence, SARIMA, forecasting model
*Corresponding author: Tel.: +66-2354-8530 Fax: +66-2354-8534
E-mail: jutatip.sil@mahidol.ac.th
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References
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[4] Bureau of Epidemiology, Ministry of Public Health, 2016. Surveillance Report (R 506) DHF Total. [online] Available at: https://www.boe.moph.go.th/boedb/surdata/506wk/ y60/d262766_4860.pdf
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[6] Bhatnagar, S., Lal, V., Gupta, S.D. and Gupta, O.P., 2012. Forecasting incidence in Rajasthan, using time Series analysis. Indian Journal Public Health, 56, 81-285
[7] Suthachana, S., Kaewnokkao, W., Suangtho, P., Sayumpurujinun, S. and Kongyu, S., 2012. Forecasting the situation of influenza in Thailand in 2012. In: Bureau of Epidemiology, Department of Diseases Control, Ministry of Public Health, Workshop Conference in Analyzed and Forecasting. Bangkok, Thailand, 12-14 September 2012. Bangkok: Ministry of Public Health.
[8] Drake, J.M., 2005. Fundamental limits to the precision of early warning systems for epidemic of infectious disease. PLoS Medicine, 2(5), e144. https://doi.org/10.1371/journal.pmed.0020144.
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[10] Parmsan, C., 2007. Influence of species, latitudes and methodologies on estimates of phonological response to global warming. Global Chang Biology, 13, 1860-1872.
[11] Loha, E. and Lindijorn, B., 2010. Model variation in prediction incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from South Ethiopia. Malaria Journal 9, 166-173.
[12] Wangdi, K., Singhasivanon, P., Silawan, T., Lawpoolsri, S., White, N.J. and Kaewkungwal, J., 2010. Development of temporal modeling for forecasting and prediction of malaria infections using time series and ARIMAX analysis: A case study in endemic district of Bhutan. Malaria Journal, 9, 251-265.
[13] Tian, L., Bi, Y., Ho, S.C., Liu, W., Liang, S. and Goggins, W.B., 2008. One-year delayed effect of fog on malaria transmission: A time series analysis in the rain forest area of Mengla County, south-west China. Malaria Journal, 7, 110-123.
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[15] Gharbi, M., Quenel, P., Gustave, J., Cassadou, S., Ruche, G.L., Girdary, L. and Marrama L., 2010. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting model using climate variables as predictors, BMC Infectious Diseases 11, 166. https://doi.org/10.1186/1471-2334-11-166.
[16] Sitepu, M.S., Kaewkungwal, J., Lupleriop, N., Soonthornworasiri, N., Silawan, T., Poungsombat, S. and Lawpoolsri, S., 2013. Temporal patterns and a disease forecasting model for dengue hemorrhagic fever in Jakarta based on 10 years of surveillance data. Southeast Asian Journal Tropical Medicine and Public Health, 44(2), 206-217.
[17] Promprou, S., Jaroensutasinee, M. and Jaroensutasinee, K., 2006. Forecasting dengue hemorrhagic fever cases in Southern Thailand using ARIMA models. Dengue Bulletin, 30, 99-106.
[18] Institute of Research, Knowledge Management and Standards for Disease Control, Ministry of Public Health, 2017. A Predictive Model for Dengue Hemorrhagic Fever Epidemics in 8 Provinces, Northern Thailand. [online] Available at: https://irem2.ddc.moph.go.th/research/4803.
[19] Schmidt, W.P., Suzuki, M., Dinh Thiem, V., White, R.G., Tsuzuki, A., Yoshida, L.M., Yanai, H., Haque, U., Tho, L. H., Duc Anh, D. and Ariyoshi, K., 2011. Population density, water supply, and the risk of dengue fever in Vietnam: Cohort study and spatial analysis. PLoS Medicine, 8(8), e1001082. https://doi.org/10.1371/ journal.pmed.1001082.
[20] Thammapalo, S., Nagao, Y., Sakamoto, W., Saengtharatip, S., Tsujitani, M., Nakamura, Y., Coleman, P.G. and Davies, C., 2008. Relationship between transmission intensity and incidence of dengue hemorrhagic fever in Thailand. PLoS Neglected Tropical Diseases, 2(7), e263. https://doi.org/10.1371/journal.pntd.0000263.
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[22] Schreiber, K.V., 2001. An investigation of relationships between climate and dengue using a water budgeting technique. International Journal of Biometeorology, 45(2), 81-89.
[23] Halide, H. and Ridd, P., 2008. A Predictive model hemorrhagic fever epidemics. International Journal of Environmental Health Research, 18(4), 253-265.
[24] Wongkoon, S., Pollar, M., Jaroensutasinee, M. and Jaroensutasinee, K., 2007. Predicting DHF incidence in Northern Thailand using time series analysis technique. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 1(8), 484-488.
[25] Silawan, T., Singhasivanon, P., Kaewkungwal, J., Nimmanitya, S. and Suwonkerd, W., 2008. Temporal patterns and forecast of dengue infection in Northeastern Thailand. Southeast Asian Journal of Tropical Medicine and Public Health, 39(1), 90-98.
[26] Dom, N.C., Hassan, A.A., Latif, Z.A. and Ismail, R., 2012. Generating temporal model using climate variables for the prediction of dengue case in Sunbang Jaya, Malaysia. Asian Pacific Journal of Tropical Disease 3(5), 352-361.
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