OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING

Authors

  • Nur Alamsyah Universitas Informatika Dan Bisnis Indonesia
  • Venia Restreva Danestiara Universitas Informatika Dan Bisnis Indonesia
  • Budiman Budiman Universitas Informatika Dan Bisnis Indonesia
  • Reni Nursyanti Universitas Informatika Dan Bisnis Indonesia
  • Elia Setiana Universitas Informatika Dan Bisnis Indonesia
  • Acep Hendra Universitas Informatika Dan Bisnis Indonesia

DOI:

https://doi.org/10.33480/techno.v22i1.6507

Keywords:

epidemic modeling, facebook prophet, hyperparameter tuning, MPOX forecasting, time series prediction

Abstract

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.

References

Alamsyah, N., Budiman, B., Yoga, T. P., & Alamsyah, R. Y. R. (2024). COMPARISON LINEAR REGRESSION AND RANDOM FOREST MODELS FOR PREDICTION OF UNDERGROUND DROUGHT LEVELS IN FOREST FIRES. Jurnal Techno Nusa Mandiri, 21(2), 81–86.

Alamsyah, N., Yoga, T. P., Budiman, B., & others. (2024). IMPROVING TRAFFIC DENSITY PREDICTION USING LSTM WITH PARAMETRIC ReLU (PReLU) ACTIVATION. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 9(2), 154–160.

An, T. J., Lee, J., Shin, M., & Rhee, C. K. (2024). Seasonality of common respiratory viruses: Analysis of nationwide time-series data. Respirology, 29(11), 985–993.

Babanejaddehaki, G., An, A., & Papagelis, M. (2024). Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources. ACM Transactions on Computing for Healthcare.

Bleichrodt, A., Luo, R., Kirpich, A., & Chowell, G. (2024). Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022–2023 mpox epidemic. Royal Society Open Science, 11(7), 240248.

Chaturvedi, M., Rodiah, I., Kretzschmar, M., Scholz, S., Lange, B., Karch, A., & Jaeger, V. K. (2024). Estimating the relative importance of epidemiological and behavioural parameters for epidemic mpox transmission: A modelling study. BMC Medicine, 22(1), 297.

Chen, Q., Zheng, X., Shi, H., Zhou, Q., Hu, H., Sun, M., Xu, Y., & Zhang, X. (2024). Prediction of influenza outbreaks in Fuzhou, China: Comparative analysis of forecasting models. BMC Public Health, 24(1), 1399.

Dash, S., Giri, S. K., Mallik, S., Pani, S. K., Shah, M. A., & Qin, H. (2024). Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet. Scientific Reports, 14(1), 5287.

Haque, S., Mengersen, K., Barr, I., Wang, L., Yang, W., Vardoulakis, S., Bambrick, H., & Hu, W. (2024). Towards development of functional climate-driven early warning systems for climate-sensitive infectious disease: Statistical models and recommendations. Environmental Research, 118568.

Hikmawati, E., & Alamsyah, N. (2024). Supervised Learning for Emotional Prediction and Feature Importance Analysis Using SHAP on Social Media User Data. Ingénierie Des Systèmes d’Information, 29(6).

Islam, M. S., Shahrear, P., Saha, G., Ataullha, M., & Rahman, M. S. (2024). Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach. Computers in Biology and Medicine, 178, 108707.

Jena, D., Sridhar, S. B., Shareef, J., Talath, S., Ballal, S., Kumar, S., Bhat, M., Sharma, S., Kumar, M. R., Chauhan, A. S., & others. (2024). Time series modelling and forecasting of Monkeypox outbreak trends Africa’s in most affected countries. New Microbes and New Infections, 62, 101526.

Maleki, N., Lundström, O., Musaddiq, A., Jeansson, J., Olsson, T., & Ahlgren, F. (2024). Future energy insights: Time-series and deep learning models for city load forecasting. Applied Energy, 374, 124067.

Mohapatra, R. K., Singh, P. K., Branda, F., Mishra, S., Kutikuppala, L. S., Suvvari, T. K., Kandi, V., Ansari, A., Desai, D. N., Alfaresi, M., & others. (2024). Transmission dynamics, complications and mitigation strategies of the current mpox outbreak: A comprehensive review with bibliometric study. Reviews in Medical Virology, 34(3), e2541.

Muñoz, M. C., Peñalba, M. A., & González, A. E. S. (2024). Analysis of aggregated load consumption forecasting in short, medium and long term horizons using dynamic mode decomposition. Energy Reports, 12, 1000–1013.

Orang, A., Berke, O., Poljak, Z., Greer, A. L., Rees, E. E., & Ng, V. (2024). Forecasting seasonal influenza activity in Canada—Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness. Zoonoses and Public Health, 71(3), 304–313.

Priyanka, T., Gowrisankar, A., & Banerjee, S. (2024). Mpox outbreak: Time series analysis with multifractal and deep learning network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(10).

Putrada, A. G., Alamsyah, N., Oktaviani, I. D., & Fauzan, M. N. (2024). LSTM For Web Visit Forecasting with Genetic Algorithm and Predictive Bandwidth Allocation. 2024 International Conference on Information Technology Research and Innovation (ICITRI), 53–58.

Singh, J., & Pandey, P. (2024). A Real-Time COVID-19 Exploratory Analysis and Outbreak Prediction System. 2024 2nd International Conference on Disruptive Technologies (ICDT), 43–50.

Syfullah, M. K., Santo Ali, M., Oishy, A. M., & Hossain, M. S. (2024). Towards Early Dengue Diagnosis in Bangladesh: A Non-Invasive Prediction Model Based on Symptoms and Local Trends. 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), 833–838.

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Published

2025-03-25

How to Cite

Alamsyah, N., Restreva Danestiara, V. ., Budiman, B., Nursyanti, R. ., Setiana, E. ., & Hendra, A. (2025). OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING . Jurnal Techno Nusa Mandiri, 22(1), 90–98. https://doi.org/10.33480/techno.v22i1.6507