Impact of public health measures to control SARS-CoV-2Outbreak: a data-driven analysis

Turbe, H.; Bjelogrlic, M.; Robert, A.; Gaudet-Blavignac, C.; Lovis, C.

With the rapid spread of the SARS-CoV-2 virus since Fall 2019, governments took variousmeasures to contain the propagation of the pandemic, declared on March, 2020. This study introduces a novel method to estimate the reproductive number using Bayesian inference with time-dependent priors. By inferring the infection dates from incidence time series, the developed approach allows direct comparison between reproductive number and introduction of public health measures in a specific country. First a specific period between the onset of the symptoms and a case being declared as dead is derived on data available in Switzerland. Focussing on the measures taken by 31 European countries, this study shows that most countries required tough state interventions with a stringency index equal to 83.6 out of 100 to reduce the reproductive number below one and hence control the development of the epidemy. In addition, it is shown that there is a direct correlation between the time taken to introduce restrictive measures and the time required to contain the spread of the epidemy with a median time of 8 days between the introduction of initial restrictive measures and the reproductive rate reducing below one.