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In February 2020, Italy has been the first country in Europe fighting against COVID-19. In March 2020, Italian government declared national lockdown. Until May 4th, people stayed in home confinement and only the so-called essential works and activities were continued. Like in other countries, both for the disease severity and for the risk of death, the higher the age of people the higher the risk. In the first months of 2020, Italy saw a very high number of deaths related to COVID-19, with a huge age effect. There is an agreement on the view that there had been also an excess of mortality and on the role of mortality as a correct way to reflect the dynamics of the virus's spread. In this paper we briefly discuss the trends of mortality during the first 4 months of 2020 according to the data by the Italian National Institute of Statistics. [ABSTRACT FROM AUTHOR]

Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The "official" approach adopted by public institutions to estimate the "excess mortality" during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in "ordinary" years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community. [ABSTRACT FROM AUTHOR]