Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs

نویسندگان

چکیده

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods evaluate likelihood generic model. Once we have likelihood, specify priors rely on Markov chain sample from posterior distribution. show how simulation outputs as inputs exercises assessment. apply Belgian data COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model captures dynamics very well, including three waves infections. (true) number new cases time-varying effective reproduction information structural vector autoregressions local projections. document additional government-mandated mobility curtailments would reduced deaths at zero cost or small terms output.

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ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2021

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.3819098