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COVID-19 pandemic has exacerbated the pre-existing vulnerabilities and inequalities in societies. In this paper we analyse the categories that have suffered more than others from the pandemic and the restrictions on social life in terms of mental health. We rely on the Serendipity project based on a survey administered between November 2021 and February 2022 to a sample of Italian physicians (n = 1281). The survey aimed to assess the perception of general practitioners, paediatricians, geriatricians, and mental health specialists (psychiatrists, neurologists, child neuropsychiatrists), about changes in the mental health of the population as an effect of the COVID-19 pandemic and the lockdown. The strategies implemented by the doctors interviewed in terms of the intensity of the prevention, emergence, and treatment of mental health interventions, and their association with physicians' characteristics and their opinions on patient vulnerability have been illustrated by means of a multiple correspondence analysis. An overall result of the survey is the consensus of doctors on the worsening of mental health in general population, especially among their patients, due to the pandemic and on the onset of new discomforts. The most exposed individuals to the risk of onset or worsening of mental disorders include women, young people, and patients with psychiatric comorbidity. The paper also illustrates the interventions put in place by the physicians and deemed necessary from a public heath response perspective, that include providing psychoeducation to the general population, improving telehealth services, and increasing financial and human resources for community-based care. [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]