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Sefah Frimpong | Applied Mathematics, University of Waterloo
Bayesian Parameterization of coupled behaviour-disease models
Mathematical models have been widely used to understand the dynamics of diseases from infectious diseases to oncology. Many infectious disease models have generally helped understand the behaviour of diseases and make predictions. However, recent data shows that the dynamics of these diseases are influenced by the behaviour of the host population. With evidence of imitation dynamics amongst the host population, the transmission of diseases changes. This work establishes that coupled behaviour-disease models give more information about the disease and improve the predictive powers of the models. We illustrate by applying it to the COVID-19 virus of selected countries while parameter estimation is performed using an Approximate Bayesian Computation (ABC) approach. We examine the predictive power of a conventional deterministic SIR model and a coupled behaviour-disease model which takes into account the seasonality of the COVID-19 virus.