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• W2953223669 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." @default.
• W2953223669 created "2019-06-27" @default.
• W2953223669 creator A5037495909 @default.
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• W2953223669 date "2011-10-21" @default.
• W2953223669 modified "2023-09-27" @default.
• W2953223669 title "Monitoring and prediction of an epidemic outbreak using syndromic observations" @default.
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