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Background: The novel coronavirus disease is an ongoing pandemic that started in China in December 2019. This paper is aimed at estimating the first two infections waves in Italy in relation to adopted health policies. Design and methods: We moved deaths of the Italian COVID-19 registry from recorded to infection date by the weighted moving average. We considered two infection fatality ratios related to the effective or saturated health system, we estimated the likely incidence curve from the resulting deaths and evaluated the curve shape before and after the national health policies. Results: From the 24th of February 2020 to the 7th of February 2021, we estimated 6,664,655 (4,639,221-9,325,138) cases distributed on two waves. Suitable daily infection fatality rates were 2.53% within the first wave and 1.15% within the second one. The first wave (February-July 2020) had its peak on the 14th of March 2020 (26,575). The second wave (August 2020-February 2021) was fatter with the peak on the 12th of November (60,425) and a hump in December before decreasing to 26,288 at the end. Adopted health policies were followed by changes in the curve rate. Conclusions: Tracing infection contacts and quarantining asymptomatic people reduced virus lethality in the second wave. Restriction on population mobility is effective within a suppression strategy, distance learning reduces contacts among families. Removal of restrictions should be implemented by sequential steps for avoiding a quick rising of incident cases. A reasonable public health daily goal to control both virus spread and lethality could be to find at least 87 cases for each death. [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]

In countries most impacted by coronavirus disease (COVID-19), such as Italy and China, surveillance reveals that the number of deaths differ by sex. Preliminary data suggest that while the distributions of cases vary by sex, men represent the larger proportion of deaths in these countries. Analyses of deaths can indicate differential disease progression between men and women more robustly than analyses of cases, as the former are less susceptible to biases of underreporting and bottlenecks in testing. Canada has an enormous opportunity to apply its sex-specific mortality data to conduct comprehensive health and medical research that captures sex-based differences in manifestation of the disease to improve outcomes and prevention methods. During the ongoing pandemic, it is difficult for complete and wholly accurate data of all COVID-19 deaths to be obtained when healthcare and public health personnel are operating at full capacity. However, it is crucial that efforts continue to be made to capture this information and make it accessible, as it can also be applied to inform implementation of more effective and equitable public health and clinical strategies, such as the dissemination of targeted health communication materials and therapy. [ABSTRACT FROM AUTHOR]