To lockdown or not to lockdown: Analysis of the EU lockdown performance vs. COVID-19 outbreak

Abstract

The worldwide COVID-19 outbreak has dramatically called for appropriate responses from governments. Scientists estimated both the basic reproduction number and the lethality of the virus. The former one depends on several factors (environment and social behavior, virus characteristics, removal rate). In the absence of specific treatments (vaccine, drugs) for COVID-19 there was a limited capability to control the likelihood of transmission or the recovery rate. Therefore, to limit the expected exponential spread of the disease and to reduce its consequences, most national authorities have adopted containment strategies that are mostly focused on social distancing measures. In this context, we performed an analysis of the effects of government lockdown policies in 5 European Countries (France, Germany, Italy, Spain, United Kingdom). We used phone mobility data, published by Apple Inc. and Google, as an indirect measure of social distancing over time since we believe they represent a good approximation of actual changes in social behaviors. (i) The responsiveness of the governments in taking decisions. (ii) The coherence of the lockdown policy with changes in mobility data. (iii) The lockdown implementation performance in each country. (iv) The effects of social distancing on the epidemic evolution. These data were first analyzed in relation with the evolution of political recommendations and directives to both assess (i) responsiveness of governments in taking decisions and (ii) the implementation performance in each country. Subsequently, we used data made available by John Hopkins University in the attempt to compare changes in people behaviors with the evolution of COVID-19 epidemic (confirmed cases, new and cumulative) in each country in scope. Finally, we made an attempt to identify some key lockdown performance parameters in order to: (i) establish responsiveness, efficiency and effectiveness of the lockdown measures. (ii) model the latency occurring between the changes in social behaviors and the changes in growth rate of the disease.