Predicting unobserved exposures from seasonal epidemic data

TL;DR

This paper introduces a stochastic modeling approach for seasonal epidemic data that accurately predicts unobserved exposures by reducing high-dimensional models to a low-dimensional manifold, capturing key outbreak dynamics.

Contribution

The authors develop a nonlinear stochastic projection method that simplifies complex epidemic models while preserving essential dynamics, enabling long-term predictions of unobserved exposures.

Findings

Accurately predicts timing and amplitude of disease outbreaks.

Captures recurrent epidemic behavior in reduced models.

Enables long-term estimation of unobserved exposures.

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.