Lifting restrictions with changing mobility and the importance of soft containment measures: A SEIRD model of COVID-19 dynamics

This paper estimates a SEIRD (susceptible-exposed-infectedrecovered-deaths) epidemic model of COVID-19, which accounts for both observed and unobserved states and endogenous mobility changes induced by lockdown policies. The model is estimated on Lombardy and London – two regions that had among the worst outbreaks of the disease in the world – and used to predict the evolution of the epidemic under different policies. We show that policies targeted also at mitigating the probability of contagion are more effective in containing the spread of the disease, than the one aimed at just gradually reducing the mobility restrictions. In particular, we show that if the probability of contagion is decreased between 20% and 40% of its original level before the outbreak, while increasing mobility, the total death toll would not be higher than in a permanent lockdown scenario. On the other hand, neglecting such policies could increase the risk of a second epidemic peak even while lifting lockdown measures at later dates. This highlights the importance during the containment of the disease of promoting “soft” policy measures that could reduce the probability of contagion, such as, wearing masks and social distancing.