COVID-19 infection-to-death lag and lockdown-effect lag in Italy and Spain

Abstract Background The European phase of the COVID (SARS-CoV-2) pandemic started in early February 2020. Lockdown measures have been adopted to flatten the infection curve. Early detection and robust estimation of its inflection point and other phenomenological epidemic parameters are extremely relevant to evaluate effectiveness of containment measures and the epidemic course. Methods We use a nonlinear least-squares algorithm to fit logistic functions to the curves of active infected and deceased during the pandemic evolution, studying also the changes and stabilization of the logistic parameters. Results We present the computation of the inflection points for Spain and Italy. We use these results to estimate two fundamental disease parameters: the infection-to-death lag and the onset of lockdown effects. These estimates are sharp and robust. Conclusions The data-driven logistic-fit approach has proven to be a remarkably powerful tool, allowing us to effectively compute three disease parameters which may prove to be an invaluable help in the control of the epidemic as well as to model response of the health services in other countries in the World. The results and methods can be used to forecast the evolution in countries still in early stages of the epidemic.