EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters
Abstract
We introduce EPI-KAN, a novel method for estimating COVID-19 time-varying parameters. EPI-KAN uses historical epidemiological data, Physics-Informed Neural Network (PINN), and the novel Kolmogorov-Arnold Network (KAN). The method harnesses the novel Kolmogorov-Arnold Network (KAN), which is a type of artificial neural network. For the KAN in this paper, we learn activation functions that are represented using Fourier series, hence we abbreviate as KAN-F. In this study, we estimate parameters in the context of an SIRD compartmental differential equations. The time-dependent parameters are the transmission rate β(t), recovery rate γ(t), and mortality rate μ(t). We define three KAN-F functions β̂, γ̂, μ̂ that model the true parameters β(t), γ(t), μ(t), respectively. We test two model architectures for the KAN-F: the first has 8 input variables consisting of S, I, R, D, and their numerical gradients at any time t, while the second has 4 input variables excluding the numerical gradients. The objective loss function that has to be minimized is subject to Physics-Informed Neural Network (PINN). Using historical data of COVID-19 from three South-East Asian countries: Indonesia, Singapore, and Malaysia, we are able to estimate β(t), γ(t), and μ(t) on each country with decent accuracy and efficiency. The time period of choice coincides with the period where SARS-CoV-2 Delta variant (B.1.617.2) was dominant. In addition to estimating the rates during the training period, we also predict transmission rates over 30 days during forecast period. We found that the output of KAN-F over the forecast period can give good predictions if we scale the output by a factor of 17\% for Indonesia and 30\% for Singapore and Malaysia.
Related
- An Efficient Implicit Tau-Leaping Method for Estimating Second-Order Sensitivities of Stiff Stochastic Biochemical Systems
- A general method for estimating reliability using Bayesian Measurement Uncertainty
- Estimating latent neuronal nonlinear dynamics by sequential Monte Carlo method and sparse modeling
- Evaluation of in vitro method for estimating in vivo digestibility in mixed-breed dogs across each life stage.
- Exploring density‐dependent relationships in demographic parameters in populations of birds at a large spatial scale
BibTeX
@misc{anbiya2026epikan,
title = {{EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters}},
author = {Arief Anbiya},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.15302},
eprint = {2607.15302},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.15302},
}
RIS
TY - UNPB
TI - EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters
AU - Arief Anbiya
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2607.15302
UR - https://arxiv.org/abs/2607.15302
AB - We introduce EPI-KAN, a novel method for estimating COVID-19 time-varying parameters. EPI-KAN uses historical epidemiological data, Physics-Informed Neural Network (PINN), and the novel Kolmogorov-Arnold Network (KAN). The method harnesses the novel Kolmogorov-Arnold Network (KAN), which is a type of artificial neural network. For the KAN in this paper, we learn activation functions that are represented using Fourier series, hence we abbreviate as KAN-F. In this study, we estimate parameters in the context of an SIRD compartmental differential equations. The time-dependent parameters are the transmission rate β(t), recovery rate γ(t), and mortality rate μ(t). We define three KAN-F functions β̂, γ̂, μ̂ that model the true parameters β(t), γ(t), μ(t), respectively. We test two model architectures for the KAN-F: the first has 8 input variables consisting of S, I, R, D, and their numerical gradients at any time t, while the second has 4 input variables excluding the numerical gradients. The objective loss function that has to be minimized is subject to Physics-Informed Neural Network (PINN). Using historical data of COVID-19 from three South-East Asian countries: Indonesia, Singapore, and Malaysia, we are able to estimate β(t), γ(t), and μ(t) on each country with decent accuracy and efficiency. The time period of choice coincides with the period where SARS-CoV-2 Delta variant (B.1.617.2) was dominant. In addition to estimating the rates during the training period, we also predict transmission rates over 30 days during forecast period. We found that the output of KAN-F over the forecast period can give good predictions if we scale the output by a factor of 17\% for Indonesia and 30\% for Singapore and Malaysia.
ER -