Corpus ID: 237940270

Uncertainty quantification in covid-19 spread: lockdown effects

@inproceedings{Carpio2021UncertaintyQI,
  title={Uncertainty quantification in covid-19 spread: lockdown effects},
  author={Arturo Carpio and E. Pierret},
  year={2021}
}
We develop a Bayesian inference framework to quantify uncertainties in epidemiological models. We use SEIJR and SIJR models involving populations of susceptible, exposed, infective, diagnosed, dead and recovered individuals to infer from covid-19 data rate constants, as well as their variations in response to lockdown measures. To account for confinement, we distinguish two susceptible populations at different risk: confined and unconfined. We show that transmission and recovery rates within… 

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