ABSTRACT
Since November 6th, 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
ABSTRACT
Since November 6th, 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 .
ABSTRACT
In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, 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, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of 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.
Subject(s)
COVID-19/metabolism , COVID-19/virology , SARS-CoV-2/metabolism , Host Microbial Interactions , Immunity/immunology , Protein Interaction Maps/physiology , Proteome , Proteomics/methods , SARS-CoV-2/pathogenicity , Severity of Illness Index , Viral Proteins/metabolism , Viral Regulatory and Accessory Proteins/metabolismABSTRACT
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 Sanità 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.
ABSTRACT
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.