Estimating Time-Dependent COVID-19 Parameters Using Kolmogorov-Arnold Network and Physics-Informed Neural Network

[2607.15302] EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters Skip to main content Search Submit Donate Log in Quantitative Biology > Other Quantitative Biology arXiv:2607.15302 (q-bio) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 12 Jul 2026 ( v1 ), last revised 13 Sep 2026 (this version, v3)] Title: EPI-KAN: A Method For Estimating and Forecasting Time-Dependent COVID-19 Parameters Authors: Arief Anbiya View PDF HTML (experimental) 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 $\beta(t)$, recovery rate $\gamma(t)$, and mortality rate $\mu(t)$. We define three KAN-F functions $\widehat{\beta}$, $\widehat{\gamma}$, $\widehat{\mu}$ that model the true parameters $\beta(t)$, $\gamma(t)$, $\mu(t)$, 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 $\beta(t)$, $\gamma(t)$, and $\mu(t)$ 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. Subjects: Other Quantitative Biology (q-bio.OT) MSC classes: 92B20, 37N25, 34A55, 92-10 Cite as: arXiv:2607.15302 [q-bio.OT] (or arXiv:2607.15302v3 [q-bio.OT] for this version) https://doi.org/10.48550/arXiv.2607.15302 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arief Anbiya [ view email ] [v1] Sun, 12 Jul 2026 23:19:50 UTC (944 KB) [v2] Tue, 25 Aug 2026 09:23:01 UTC (2,063 KB) [v3] Sun, 13 Sep 2026 09:37:26 UTC (2,063 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.OT < prev | next > new | recent | 2026-07 Change to browse by: q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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