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Anatomy of the first six months of COVID-19 vaccination campaign in Italy.
Gozzi, Nicolò; Chinazzi, Matteo; Davis, Jessica T; Mu, Kunpeng; Pastore Y Piontti, Ana; Ajelli, Marco; Perra, Nicola; Vespignani, Alessandro.
  • Gozzi N; Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom.
  • Chinazzi M; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Davis JT; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Mu K; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Pastore Y Piontti A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Ajelli M; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, United States of America.
  • Perra N; Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom.
  • Vespignani A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
PLoS Comput Biol ; 18(5): e1010146, 2022 05.
Article in English | MEDLINE | ID: covidwho-1865329
ABSTRACT
We analyze the effectiveness of the first six months of vaccination campaign against SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mobility, vaccines data, as well as estimates of the introduction and spreading of the more transmissible Alpha variant. We consider six sub-national regions and study the effect of vaccines in terms of number of averted deaths, infections, and reduction in the Infection Fatality Rate (IFR) with respect to counterfactual scenarios with the actual non-pharmaceuticals interventions but no vaccine administration. Furthermore, we compare the effectiveness in counterfactual scenarios with different vaccines allocation strategies and vaccination rates. Our results show that, as of 2021/07/05, vaccines averted 29, 350 (IQR [16, 454-42, 826]) deaths and 4, 256, 332 (IQR [1, 675, 564-6, 980, 070]) infections and a new pandemic wave in the country. During the same period, they achieved a -22.2% (IQR [-31.4%; -13.9%]) IFR reduction. We show that a campaign that would have strictly prioritized age groups at higher risk of dying from COVID-19, besides frontline workers and the fragile population, would have implied additional benefits both in terms of avoided fatalities and reduction in the IFR. Strategies targeting the most active age groups would have prevented a higher number of infections but would have been associated with more deaths. Finally, we study the effects of different vaccination intake scenarios by rescaling the number of available doses in the time period under study to those administered in other countries of reference. The modeling framework can be applied to other countries to provide a mechanistic characterization of vaccination campaigns worldwide.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1010146

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2022 Document Type: Article Affiliation country: Journal.pcbi.1010146