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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.

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An approximate analytical model is presented to investigate sound transmission, reflection and absorption of a rubber-like medium comprising a single layer of periodic cylindrical voids attached to a steel backing. The layer of voids is modelled as a homogeneous medium with effective material and geometric properties. A numerical model based on the finite element method is developed to validate results from the homogenization model, as well as to show further insights into the physical mechanisms associated with the system acoustic performance. Monopole resonance of the voids is shown to reduce sound transmission through the voided medium due to increased reflection, resulting in poor sound absorption around this frequency. Peaks of high sound absorption are attributed to Fabry-Pérot resonance with the frequency of the first peak derivable by a lumped spring-mass analogy. Sound absorption for a single layer of voids in a soft elastic medium with a steel backing is shown to be similar to the sound absorption in the same elastic medium but without the steel backing, for a single layer of voids and its mirror image in the direction of sound propagation.

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