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Register a new userBayesian Non-Parametric Inference for Infectious Disease Data
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Added by
Theodore Kypraios
Publication date
2014
fields
Mathematical Statistics
and research's language is
English
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We propose a framework for Bayesian non-parametric estimation of the rate at which new infections occur assuming that the epidemic is partially observed. The developed methodology relies on modelling the rate at which new infections occur as a function which only depends on time. Two different types of prior distributions are proposed namely using step-functions and B-splines. The methodology is illustrated using both simulated and real datasets and we show that certain aspects of the epidemic such as seasonality and super-spreading events are picked up without having to explicitly incorporate them into a parametric model.
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