Uncertainty quantification in covid-19 spread: lockdown effects

TL;DR

This paper introduces a Bayesian framework to quantify uncertainties in COVID-19 spread models, analyzing lockdown effects and population confinement, with application to Spanish data, highlighting the importance of confinement and distancing measures.

Contribution

It develops a novel Bayesian inference approach for epidemiological models that accounts for confinement and asymptomatic populations, providing uncertainty quantification from real data.

Findings

Lockdowns delay but do not stop COVID-19 spread without sufficient confinement.

Transmission and recovery rates vary with confinement and other factors.

Quantified uncertainty helps understand epidemic evolution and intervention impacts.

Abstract

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 them vary in response to facts. A key unknown to predict the evolution of the epidemic is the fraction of the population affected by the virus, including asymptomatic subjects. Our study tracks its time evolution with quantified uncertainty from available official data from the onset of the epidemic, limited, however, by the data quality. We exemplify the technique with data from Spain, country in which late drastic lockdowns were enforced for months. In late actions and in the absence of other measures, spread is delayed but not stopped unless a large enough fraction of the population is confined until the asymptomatic population is depleted. To some extent, confinement could be replaced by strong distancing through masks in adequate circumstances.