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      Metastable states in plateaus and multi-wave epidemic dynamics of Covid-19 spreading in Italy

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          Abstract

          The control of Covid 19 epidemics by public health policy in Italy during the first and the second epidemic waves has been driven by using reproductive number R t(t) to identify the supercritical (percolative), the subcritical (arrested), separated by the critical regime. Here we show that to quantify the Covid-19 spreading rate with containment measures there is a need of a 3D expanded parameter space phase diagram built by the combination of R t(t) and doubling time T d(t). In this space we identify the Covid-19 dynamics in Italy and its administrative Regions. The supercritical regime is mathematically characterized by (i) the power law of T d vs. [R t(t) − 1] and (ii) the exponential behaviour of T d vs. time, either in the first and in the second wave. The novel 3D phase diagram shows clearly metastable states appearing before and after the second wave critical regime. for loosening quarantine and tracing of actives cases. The metastable states are precursors of the abrupt onset of a next nascent wave supercritical regime. This dynamic description allows epidemics predictions needed by policymakers interested to point to the target " zero infections" with the elimination of SARS-CoV-2, using the Finding mobile Tracing policy joint with vaccination-campaign, in order to avoid the emergence of recurrent new variants of SARS-CoV-2 virus , accompined by recurrent long lockdowns, with large economical losses, and large number of fatalities.

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          Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

          The newly emergent human virus SARS-CoV-2 is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact-tracing needed to stop the epidemic. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.
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            Analysis and forecast of COVID-19 spreading in China, Italy and France

            Highlights • Epidemic spreading • COVID19 • SIR model • Recursive relations and non linear fitting
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              Contact Tracing during Coronavirus Disease Outbreak, South Korea, 2020

              We analyzed reports for 59,073 contacts of 5,706 coronavirus disease (COVID-19) index patients reported in South Korea during January 20–March 27, 2020. Of 10,592 household contacts, 11.8% had COVID-19. Of 48,481 nonhousehold contacts, 1.9% had COVID-19. Use of personal protective measures and social distancing reduces the likelihood of transmission.
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                Author and article information

                Contributors
                antonio.bianconi@ricmass.eu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 June 2021
                14 June 2021
                2021
                : 11
                : 12412
                Affiliations
                [1 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Institute of Crystallography, , Consiglio Nazionale delle Ricerche CNR, ; via Salaria Km 29.300, Monterotondo, 00015 Rome, Italy
                [2 ]GRID grid.499323.6, Rome International Centre Materials Science Superstripes RICMASS, ; via dei Sabelli 119A, 00185 Rome, Italy
                [3 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Institute for Microelectronics and Microsystems IMM, , Consiglio Nazionale delle Ricerche CNR, ; Via del Fosso del Cavaliere 100, 00133 Rome, Italy
                [4 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Istituto di Farmacologia Traslazionale IFT, , Consiglio Nazionale delle Ricerche CNR, ; Via del Fosso del Cavaliere 100, 00133 Rome, Italy
                [5 ]GRID grid.463190.9, ISNI 0000 0004 0648 0236, INFN - Laboratori Nazionali di Frascati, ; 00044 Frascati, RM Italy
                [6 ]GRID grid.5602.1, ISNI 0000 0000 9745 6549, School of Pharmacy, Physics Unit, , University of Camerino, ; 62032 Camerino, MC Italy
                [7 ]GRID grid.183446.c, ISNI 0000 0000 8868 5198, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), ; Moscow, Russia 115409
                Author information
                http://orcid.org/0000-0001-9845-9394
                http://orcid.org/0000-0002-6615-2264
                http://orcid.org/0000-0002-3901-9230
                http://orcid.org/0000-0002-7741-4979
                http://orcid.org/0000-0002-8138-7547
                http://orcid.org/0000-0002-4914-4975
                http://orcid.org/0000-0001-9795-3913
                Article
                91950
                10.1038/s41598-021-91950-5
                8203777
                34127760
                e71abdc1-f6f6-4808-a698-075d644ba6ca
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 February 2021
                : 27 May 2021
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                © The Author(s) 2021

                Uncategorized
                epidemiology,scientific data
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                epidemiology, scientific data

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