Seasonal Time Series Models for Forecasting Pertussis Endemic with Holt-Winter's Exponential Smoothing

Ashri Shabrina Afrah (Program Studi Teknik Informatika UIN Maulana Malik Ibrahim Malang)
Nur Fitriyah Ayu Tunjung Sari (Universitas Islam Negeri Maulana Malik Ibrahim Malang)
Shoffin Nahwa Utama (Universitas Islam Negeri Maulana Malik Ibrahim Malang)
Tri Mukti Lestari (Universitas Islam Negeri Maulana Malik Ibrahim Malang)

Walisongo Journal of Information Technology
Vol. 7 No. 2 (2025): Walisongo Journal of Information Technology

Accurate forecasting of pertussis cases can support disease surveillance and public health planning. This study evaluates the application of Holt–Winters exponential smoothing to forecast weekly pertussis cases in the United States. The study uses weekly case data reported by the Centers for Disease Control and Prevention (CDC) and the Florida Department of Health, covering the period from 2008 to 2012, with four additional observations from the final weeks of 2007 included as initial data. Two Holt–Winters configurations were evaluated using seasonal periods of 4 and 13 weeks. The dataset was divided into training and testing sets at a ratio of 80:20, and forecasting accuracy was assessed using the Mean Absolute Percentage Error (MAPE). The reported results show that the 4-week configuration achieved a MAPE of 34.7898%, whereas the 13-week configuration achieved a MAPE of 40.7340%. Thus, the 4-week configuration produced a lower MAPE than the 13-week configuration for the data evaluated in this study. However, the results should be interpreted within the scope of the selected dataset and seasonal configurations. Further evaluation using alternative seasonal periods, clearly specified testing procedures, and additional datasets is necessary to determine the robustness and generalizability of the forecasting performance.