Data assimilation for predicting the dynamics of acute respiratory infections using the ensemble Kalman filter
DOI:
https://doi.org/10.12928/bamme.v6i1.14695Keywords:
ARI, data assimilation, ensemble Kalman filterAbstract
Acute respiratory infection (ARI) is one of the most pressing public health problems due to its high transmission rate and the potential to cause significant pressure on health services. This study applies the Ensemble Kalman Filter (EnKF) method to predict the spread of ARI with a three-compartment population model, namely Susceptible (S), Exposed (E), and Infected (I). This study shows that the EnKF method can predict the spread of ARI well. The number of ensembles used affects the level of accuracy. The EnKF provides accurate predictions of the dynamics of ARI spread, making it relevant as a scientific basis in the formulation of data-based mitigation strategies. It can provide a scientific basis for policymakers to formulate accurate and measurable preventive measures.
References
de Korte, C. W. E., Verlaan, M., & Heemink, A. W. (2025). A wave data assimilation system for the North Sea based on Ensemble Kalman Filtering and the potential of satellite altimetry. Ocean Modelling, 197, 102586. https://doi.org/10.1016/j.ocemod.2025.102586
Diniz, P. S. R. (2020). Adaptive Filtering Algorithms and Practical Implementation Fifth Edition. https://doi.org/10.1007/978-3-030-29057-3
El Serafy, G. Y., & Mynett, A. E. (2004). Comparison of EKF and EnKF in Sobek river: Case study maxau-ijssel. Hydroinformatics, 513–520.
Engbert, R., Rabe, M. M., Kliegl, R., & Reich, S. (2021). Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics. Bulletin of Mathematical Biology, 83(1). https://doi.org/10.1007/s11538-020-00834-8
Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5). https://doi.org/10.1029/94jc00572
Nasution, A. F., Lubis, R. S., & Widyasari, R. (2023). Implementation of the Generalized Space Time Autoregressive (GSTAR) model in the case of the spread of coronavirus in the district city of North Sumatra. ZERO: Jurnal Sains, Matematika dan Terapan, 6(2), 157-166.
Goraahe, N., Haruna, N., Sakinah4, A. I., & Ahmad, A. (2025). Profil bakteri dari usap tenggorok penderita infeksi saluran pernapasan usia sekolah dasar di Puskesmas Tamalanrea Makassar. Jurnal Riset Rumpun Ilmu Kedokteran, 4(1), 1–9. https://doi.org/10.55606/jurrike.v4i1.4313
Inderiati, D., Rachmawaty, T., & Amaniah Anhar, C. (2023). Identification of acute respiratory infection patients using RP2 nested multiplex PCR test in Jakarta, Indonesia. Medical Laboratory Technology Journal, 8(2), 53-62. https://doi.org/10.31964/mltj.v8i2.519
Islam, M. S., Hoque, M. E., & Amin, M. R. (2020). Integration of Kalman filter in the epidemiological model: a robust approach to predict COVID-19 outbreak in Bangladesh. International Journal of Modern Physics C, 32(8), 2150108. https://doi.org/10.1142/S0129183121501084
Jang, J., Jang, K., Kwon, H. D., & Lee, J. (2018). Feedback control of an HBV model based on ensemble kalman filter and differential evolution. Mathematical Biosciences and Engineering, 15(3), 667–691. https://doi.org/10.3934/mbe.2018030
Jin, Y., Chen, G., & Tian, Z. (2026). Investigation of ensemble Kalman filter-based data assimilation for turbulent flow in a 3×3 rod bundle. Nuclear Engineering and Technology, 58(2), 103899. https://doi.org/10.1016/j.net.2025.103899
Katzfuss, M., Stroud, J. R., & Wikle, C. K. (2016). Understanding the ensemble Kalman filter. The American Statistician, 70(4), 350-357. https://doi.org/10.1080/00031305.2016.1141709
Keller, J., Hendricks Franssen, H. J., & Marquart, G. (2018). Comparing seven variants of the ensemble Kalman filter: How many synthetic experiments are needed? Water Resources Research, 54(9), 6299–6318. https://doi.org/10.1029/2018WR023374
Kumari, N., & Sharma, S. (2018). Modeling the dynamics of infectious disease under the influence of environmental pollution. International Journal of Applied and Computational Mathematics, 4(3). https://doi.org/10.1007/s40819-018-0514-x
Liu, J., Kirkland, L., & Srivastava, J. (2024). A Kalman filter based framework for monitoring the performance of in-hospital mortality prediction models over time. http://arxiv.org/abs/2402.06812
Moldovan, G., Lehnasch, G., Cordier, L., & Meldi, M. (2021). A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows. Journal of Computational Physics, 443, 110481.
Pedroso, L., Batista, P., Oliveira, P., & Silvestre, C. (2022). Discrete-time distributed Kalman filter design for networks of interconnected systems with linear time-varying dynamics. International Journal of Systems Science, 53(6), 1334–1351. https://doi.org/10.1080/00207721.2021.2002461
Rahmadanti, D., & Alnur, R. D. (2023). Faktor-faktor yang berhubungan dengan kejadian ISPA pada balita. Jurnal Sains dan Kesehatan, 2(2), 63-70. https://doi.org/10.57151/jsika.v2i2.266
Rif'an, M., Yusivar, F., Wahab, W., & Kusumoputro, B. (2015). A comparison of ensemble Kalman filter and extended Kalman filter as the estimation system in sensorless BLDC motor. ARPN Journal of Engineering and Applied Sciences, 10(17), 7386-7393.
Roth, M., Hendeby, G., Fritsche, C., & Gustafsson, F. (2017). The ensemble Kalman filter: A signal processing perspective. Eurasip Journal on Advances in Signal Processing, 2017(1). https://doi.org/10.1186/s13634-017-0492-x
Sánchez-León, E., Erdal, D., Leven, C., & Cirpka, O. A. (2020). Comparison of two ensemble Kalman-based methods for estimating aquifer parameters from virtual 2-D hydraulic and tracer tomographic tests. Geosciences (Switzerland), 10(7), 1–27. https://doi.org/10.3390/geosciences10070276
van Leeuwen, P. J. (2020). A consistent interpretation of the stochastic version of the ensemble Kalman filter. Quarterly Journal of the Royal Meteorological Society, 146(731), 2815–2825. https://doi.org/10.1002/qj.3819
Xincun, Y., Yongzhong, O., Fuping, S., & Hui, F. (2013). Kalman filter applied in underwater integrated navigation system. Geodesy and Geodynamics, 4(1), 46-50.
Zhu, X., Gao, B., Zhong, Y., Gu, C., & Choi, K. S. (2021). Extended Kalman filter based on stochastic epidemiological model for COVID-19 modelling. Computers in Biology and Medicine, 137. https://doi.org/10.1016/j.compbiomed.2021.104810
Zulfi, M., Hasan, M., & Purnomo, K. D. (2018). The development rainfall forecasting using kalman filter. Journal of Physics: Conference Series, 1008(1). https://doi.org/10.1088/1742-6596/1008/1/012006
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Copyright (c) 2026 Yolanda Norasia, Dinni Rahma Oktaviani, Aini Fitriyah, Devi Marita Putri
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