Bayesian semiparametric time varying model for count data to study the spread of the COVID-19 cases
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Recent outbreak of the novel corona virus COVID-19 has affected all of our lives in one way or the other. While medical researchers are working hard to find a cure and doctors/nurses to attend the affected individuals, measures such as `lockdown', `stay-at-home', `social distancing' are being implemented in different parts of the world to curb its further spread. To model this spread which is assumed to be a non-stationary count-valued time series, we propose a novel time varying semiparametric AR(p) model for the count valued data of newly affected cases, collected every day. We calculate posterior contraction rate of the proposed Bayesian model. Our proposed structure of the model is amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We show excellent performance in simulations. Our method is then applied on the daily time series data of newly confirmed cases to study its spread through different government interventions.
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