Monitoring and prediction of an epidemic outbreak using syndromic observations

TL;DR

This paper introduces a stochastic nonlinear filtering algorithm using a particle filter for syndromic surveillance, enabling early epidemic peak prediction based on non-medical observations.

Contribution

It presents a novel epidemic monitoring framework combining compartmental models with particle filtering for real-time outbreak prediction.

Findings

Framework provides early epidemic peak prediction.

Effective with moderate prior knowledge uncertainty.

Utilizes non-medical syndromic data for estimation.

Abstract

The paper presents an algorithm for syndromic surveillance of an epidemic outbreak formulated in the context of stochastic nonlinear filtering. The dynamics of the epidemic is modeled using a generalized compartmental epidemiological model with inhomogeneous mixing. The syndromic (typically non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study etc.) are used for estimation. The state of the epidemic, including the number of infected people and the unknown parameters of the model, are estimated via a particle filter. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.