Forecasting Typhoid Fever Cases using Decomposition Method, Holt-Winter’s Method, and Sarima Method
Keywords:
Typhoid Fever, Time Series, Decomposition Method, Holt-Winter Method, SARIMA Method.Abstract
This study compares the forecasting accuracy of the three univariate time series models, Decomposition, Winter’s and Seasonal autoregressive integrated moving average (SARIMA) method to predict also monthly typhoid fever cases. Secondary data is collected from Federal Medical Centre Jalingo and explored with the three time series models. The study applied time series decomposition method, Holt-Winter’s method, and SARIMA method to compare their forecasting accuracy using Root Mean Square Error (RMSE) as the selection criterion. The results for typhoid fever cases revealed that SARIMA (4,0,0) 
(1,0,0) best fits and predict the data more than the Decomposition, and the two Holt-Winter’s model. The monthly Typhoid fever indices indicates that the month of April, May and July to October has higher typhoid fever cases compared to the annual average. The highest index is in the month of August accounting for nearly 6.6% increase over the annual average.
References
1) Anwar, M., Khan, M. A., & Rizvi, S. A. (2016). Time series analysis of infectious diseases: A review. International Journal of Epidemiology Research, 3(2), 101–110.
2) Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.
3) Chatfield, C. (2004). The Analysis of Time Series: An Introduction (6th ed.). CRC Press.
4) Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.
5) Crump, J. A., Luby, S. P., & Mintz, E. D. (2004). The global burden of typhoid fever. Bulletin of the World Health Organization, 82(5), 346–353.
6) Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts.
7) Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
8) Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). Texts.
9) Ogunmola, A. O., & Jibo, Y. N. (2025). Comparing Univariate Time Series Forecast Methods for Malaria Fever Cases. Journal of Multidisciplinary Sciences, 3(2), 1018–1030. https://doi.org/10.58578/mikailalsys.v3i2.6366
10) Ugboko, H. U., Deekae, S., & Nwachukwu, E. (2020). Typhoid fever in Nigeria: A review of its epidemiology and management. African Journal of Clinical and Experimental Microbiology, 21(1), 1–10.
11) WHO. (2023). Typhoid fever fact sheet. World Health Organization. Retrieved from https://www.who.int/news-room/fact-sheets/detail/typhoid
12) Xian, X., Wang, L., Wu, X., Tang, X., Zhai, X., Yu, R., Qu, L., & Ye, M. (2023). Comparison of SARIMA model, Holt-Winters model and ETS model in predicting the incidence of foodborne disease. BMC Infectious Diseases, 23, Article 803. https://doi.org/10.1186/s12879-023-08799-4
13) Zhang, X., Liu, Y., Yang, M., Zhang, T., Young, A. A., & Li, X. (2013). Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLOS ONE, 8(5), e63116. https://doi.org/10.1371/journal.pone.0063116
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Copyright (c) 2025 Adeniyi Oyewole Ogunmola, Yunusa Namale Jibo (Author)
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