Monitoring and prediction of an epidemic outbreak using syndromic observations.

The paper presents a method for syndromic surveillance of an epidemic outbreak due to an emerging disease, formulated in the context of stochastic nonlinear filtering. The dynamics of the epidemic is modeled using a stochastic 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 assumed available for monitoring and prediction of the epidemic. 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.