A statistical model to identify excess mortality in Italy in the period 2011-2022 (preprint)

Excess mortality is defined as an increase in the number of deaths above what is expected based on historical trends, hereafter called baseline. In a previous paper, we introduced a statistical method that allows an unbiased and robust determination of the baseline to be used for the computation of excesses. A good determination of the baseline allows us to efficiently evaluate the excess of casualties that occurred in Italy in the last 12 years and in particular in the last 3 years due to the Coronavirus Disease 2019 (COVID-19) epidemic. To this extent, we have analyzed the data on mortality in Italy in the period January 1st 2011 to December 31th 2022, provided by the Italian National Institute of Statistics (ISTAT). The dataset contains information on deaths for all possible causes, without specific reference to any particular one. The data exhibit strikingly evident periodicity in the number of deaths with pronounced maxima in the winter and minima in the summer, repeating itself in amplitude along the whole twelve-year sample. Superimposed on this wave-like structure are often present excesses of casualties, most likely due to occasional causes of death such as the flu epidemics (in winter) and heat waves (in summer). The very accurate periodicity along the seasons (the "baseline"), allows us to determine with great accuracy and confidence the number of expected deaths for each day of the year in the absence of occasional contributions. Each of the latter can be modeled with an additional function that parameterizes the deviation from the baseline.

Study on the effects of the restrictive measures for containment of the COVID-19 pandemic on the reproduction number $R_t$ in Italian regions (preprint)

Since November 6$^{\mathrm{th}}$, 2020, Italian regions have been classified according to four levels, corresponding to specific risk scenarios, for which specific restrictive measures have been foreseen. By analyzing the time evolution of the reproduction number $R_t$, we estimate how much different restrictive measures affect $R_t$, and we quantify the combined effect of the diffusion of virus variants and the beginning of the vaccination campaign upon the $R_t$ trend. We also compute the time delay between implementation of restrictive measures and the resulting effects. Three different models to describe the effects of restrictive measures are discussed and the results are cross-checked with two different algorithms for the computation of $R_t$.

A statistical analysis of death rates in Italy for the years 2015-2020 and a comparison with the casualties reported for the COVID-19 pandemic (preprint)

We analyze the data about casualties in Italy in the period 01/01/2015 to 30/09/2020 released by the Italian National Institute of Statistics (ISTAT). The data exhibit a clear sinusoidal behavior, whose fit allows for a robust subtraction of the baseline trend of casualties in Italy, with a surplus of mortality in correspondence to the flu epidemics in winter and to the hottest periods in summer. While these peaks are symmetric in shape, the peak in coincidence with the COVID-19 pandemics is asymmetric and more pronounced. We fit the former with a Gaussian function and the latter with a Gompertz function, in order to quantify number of casualties, the duration and the position of all causes of excess deaths. The overall quality of the fit to the data turns out to be very good. We discuss the trend of casualties in Italy by different classes of ages and for the different genders. We finally compare the data-subtracted casualties as reported by ISTAT with those reported by the Italian Department for Civil Protection (DPC) relative to the deaths directly attributed to COVID-19, and we discuss the differences.

A study on the possible merits of using symptomatic cases to trace the development of the COVID-19 pandemic (preprint)

In a recent work we introduced a novel method to compute the effective reproduction number $R_t$ and we applied it to describe the development of the COVID-19 outbreak in Italy. The study is based on the number of daily positive swabs as reported by the Italian Dipartimento di Protezione Civile. Recently, the Italian Istituto Superiore di Sanit\`a made available the data relative of the symptomatic cases, where the reporting date is the date of beginning of symptoms instead of the date of the reporting of the positive swab. In this paper we will discuss merits and drawbacks of this data, quantitatively comparing the quality of the pandemic indicators computed with the two samples.

A simplified estimate of the Effective Reproduction Number $R_t$ using its relation with the doubling time and application to Italian COVID-19 data (preprint)

A simplified method to compute $R_t$, the Effective Reproduction Number, is presented. The method relates the value of $R_t$ to the estimation of the doubling time performed with a local exponential fit. The condition $R_t = 1$ corresponds to a growth rate equal to zero or equivalently an infinite doubling time. Different assumptions on the probability distribution of the generation time are considered. A simple analytical solution is presented in case the generation time follows a gamma distribution.

Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome. (preprint)

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level. Host conditions and comorbidities were identified as risk factors for severe and fatal disease courses, but the mechanisms of interaction between host and SARS-CoV-2 determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred on published PPI, using an explorative algorithm (Random Walk with Restart) triggered by all the 28 proteins of SARS-CoV-2, or each single viral protein one-by-one. The functional analysis for all proteins, linked to many aspects of COVID-19 pathogenesis, allows to identify the subcellular districts, where SARS-CoV-2 proteins seem to be distributed, while in each interactome built around one single viral protein, a different response was described, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, an explorative network-based approach was applied to Bradykinin Storm, highlighting a possible direct action of ORF3a and NS7b to enhancing this condition. This network-based model for SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.