Predicting unobserved exposures from seasonal epidemic data

2013/09/10 by Eric Forgoston, Forgoston, Eric, Ira B. Schwartz +1
Environmental Science · Mathematics · Medicine · #Biological Physics (physics.bio-ph) #COVID-19 epidemiological studies #Ecosystem dynamics and resilience #FOS: Biological sciences #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Populations and Evolution (q-bio.PE)

paper · pdf · doi:10.48550/arxiv.1309.2600

openalex publication_date 2013/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider a stochastic Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model with a contact rate that fluctuates seasonally. Through the use of a nonlinear, stochastic projection, we are able to analytically determine the lower dimensional manifold on which the deterministic and stochastic dynamics correctly interact. Our method produces a low dimensional stochastic model that captures the same timing of disease outbreak and the same amplitude and phase of recurrent behavior seen in the high dimensional model. Given seasonal epidemic data consisting of the number of infectious individuals, our method enables a data-based model prediction of the number of unobserved exposed individuals over very long times.

Citations

• Random dynamical systems

Related

• An Exploration of Modeling Approaches for Capturing Seasonal Transmission in Stochastic Epidemic Models
• Mathematical analysis of a delayed SEIRDS epidemics models: deterministic and stochastic approach
• A computationally efficient framework for realistic epidemic modelling through Gaussian Markov random fields
• Distributions of prevalence and daily new cases in a stochastic linear SEIR model
• Applied Probability Insights into Nonlinear Epidemic Dynamics with Independent Jumps