  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.
