Selected work

Representative Papers

On the simultaneous inference of susceptibility distributions and intervention effects from epidemic curves

Oct 26, 2025

This paper addresses the challenge of simultaneously inferring individual susceptibility heterogeneity and intervention effects from epidemic curves. We propose an identifiable joint inversion framework based on an extended SEIR model that incorporates individual-level variation in susceptibility and exposure. Using maximum likelihood estimation, we innovatively fit multiple epidemic datasets jointly to estimate shared parameters—thereby resolving the fundamental identifiability limitations inherent in conventional variable-susceptibility models. To our knowledge, this is the first rigorous validation of parameter inferability for such models. Our results demonstrate that, under realistic data quality conditions, the framework accurately recovers the true susceptibility distribution and key intervention-effect parameters. The method substantially improves the reliability of epidemic forecasting and the precision of public health policy evaluation. By establishing both theoretical guarantees and empirical evidence, it provides a foundation for integrating population heterogeneity into data-driven public health decision-making.

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Data-driven approximation of transfer operators for mean-field stochastic differential equations

Sep 11, 2025

This work addresses McKean–Vlasov-type mean-field stochastic differential equations (SDEs), for which no systematic spectral operator-theoretic framework previously existed. We extend transfer operator theory—traditionally developed for linear, local dynamics—to this nonlinear, nonlocal setting. Our method combines extended dynamic mode decomposition (EDMD) with Galerkin projection, leveraging particle-based simulations and kernel-based basis construction to yield a finite-dimensional spectral approximation of the associated Koopman (or Perron–Frobenius) transfer operator. Unlike conventional linearization or moment-closure approaches, our data-driven framework robustly identifies slow spatiotemporal modes and metastable structures directly from simulation data. We validate the method on the Cormier model, the Kuramoto model, and its three-dimensional extension, demonstrating accuracy, scalability, and numerical robustness. This constitutes the first computationally tractable, operator-spectral paradigm for global dynamical analysis of complex mean-field systems, grounded rigorously in functional-analytic spectral theory.

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Recent publications

Latest Papers

On the simultaneous inference of susceptibility distributions and intervention effects from epidemic curves

Oct 26, 2025

This paper addresses the challenge of simultaneously inferring individual susceptibility heterogeneity and intervention effects from epidemic curves. We propose an identifiable joint inversion framework based on an extended SEIR model that incorporates individual-level variation in susceptibility and exposure. Using maximum likelihood estimation, we innovatively fit multiple epidemic datasets jointly to estimate shared parameters—thereby resolving the fundamental identifiability limitations inherent in conventional variable-susceptibility models. To our knowledge, this is the first rigorous validation of parameter inferability for such models. Our results demonstrate that, under realistic data quality conditions, the framework accurately recovers the true susceptibility distribution and key intervention-effect parameters. The method substantially improves the reliability of epidemic forecasting and the precision of public health policy evaluation. By establishing both theoretical guarantees and empirical evidence, it provides a foundation for integrating population heterogeneity into data-driven public health decision-making.

0 citationsRead paper

Data-driven approximation of transfer operators for mean-field stochastic differential equations

Sep 11, 2025

This work addresses McKean–Vlasov-type mean-field stochastic differential equations (SDEs), for which no systematic spectral operator-theoretic framework previously existed. We extend transfer operator theory—traditionally developed for linear, local dynamics—to this nonlinear, nonlocal setting. Our method combines extended dynamic mode decomposition (EDMD) with Galerkin projection, leveraging particle-based simulations and kernel-based basis construction to yield a finite-dimensional spectral approximation of the associated Koopman (or Perron–Frobenius) transfer operator. Unlike conventional linearization or moment-closure approaches, our data-driven framework robustly identifies slow spatiotemporal modes and metastable structures directly from simulation data. We validate the method on the Cormier model, the Kuramoto model, and its three-dimensional extension, demonstrating accuracy, scalability, and numerical robustness. This constitutes the first computationally tractable, operator-spectral paradigm for global dynamical analysis of complex mean-field systems, grounded rigorously in functional-analytic spectral theory.

0 citationsRead paper