[學術演講] Inference under Dynamical Survival Analysis Models of Stochastic Epidemics

Inference under Dynamical Survival Analysis Models of Stochastic Epidemics

Date : 2026/07/07 (Tue.) 11:00~12:00
Location : Room 639, Institute of Mathematics (NTU Campus)
Speaker : Jason Xu
Abstract : Stochastic compartmental models are prevalent tools for describing disease spread, but inference under these models is challenging for many types of surveillance data when the marginal likelihood function becomes intractable due to missing information. Viewed as a continuous-time Markov process, quantities such as transition probabilities require an intractable marginalization or integration over the infinitely many paths consistent with observed endpoints. To address this, we develop a closed-form likelihood for discretely observed incidence count data under the dynamical survival analysis (DSA) paradigm. The method approximates the stochastic population-level hazard by a large population limit while retaining a count-valued stochastic model, and leads to survival analytic inferential strategies that are both computationally efficient and flexible to model generalizations. Through simulation, we show that parameter estimation is competitive with recent exact but computationally expensive likelihood-based methods in partially observed settings, and that the method is extensible to frailty models and network-based variants.

Mathmatics

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