PERSPECTIVE article
Front. Appl. Math. Stat.
Sec. Mathematical Biology
This article is part of the Research TopicIntegrating Modeling Methods for Quantifying Uncertainty in Infectious Disease ModelsView all articles
Iterative Calibration of Medical Digital Twins via Adaptive Estimators
Provisionally accepted
- 1Systems Medicine of Infectious Diseases, University of Idaho, Moscow, United States
- 2Universidad Autonoma de Nuevo Leon, San Nicolás de los Garza, Mexico
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While iterative calibration of computational models is a fundamental aspect of digital twins, it has been largely overlooked. Instead of focusing on parameter identification for static models, the implementation of digital twins requires not only high-resolution computational models but also the ability to assimilate patient-specific data continuously. Here, we review adaptive estimator algorithms and how this can address iterative calibration of digital twins, enabling the estimation of unmeasurable states while continuously adapting model parameters. Integrating adaptive estimators into digital twins offers a paradigm shift: transforming them from static representations into living, evolving systems that advance personalized medicine.
Keywords: adaptive estimators, Filters, medical digital twins, Observers, personalized medicine
Received: 04 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Sereno, Neujahr, Hernandez-Gonzalez and Hernandez-Vargas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Esteban A. Hernandez-Vargas
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