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

Fernández-Villaverde, Jesús, Jonas Arias, Rubio-Ramírez, Juan Francisco and Minchul Shin
Authors registered in the RePEc Author Service: Jesus Fernandez-Villaverde

No 15951, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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

Keywords: Bayesian estimation; Epidemiological models; Causality; Policy interventions (search for similar items in EconPapers)
JEL-codes: C1 C5 I1 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs (2021)
Working Paper: Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs (2021)
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