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Bellomarini L, Bencivelli L, Biancotti C, Blasi L, Conteduca FP, Gentili A, Laurendi R, Magnanimi D, Zangrandi MS, Tonelli F, Ceri S, Benedetto D, Nissl M, Sallinger E. Reasoning on company takeovers: From tactic to strategy. DATA KNOWL ENG 2022. [DOI: 10.1016/j.datak.2022.102073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Marchetti S, Borin A, Conteduca FP, Ilardi G, Guzzetta G, Poletti P, Pezzotti P, Bella A, Stefanelli P, Riccardo F, Merler S, Brandolini A, Brusaferro S. An epidemic model for SARS-CoV-2 with self-adaptive containment measures. PLoS One 2022; 17:e0272009. [PMID: 35877667 PMCID: PMC9312378 DOI: 10.1371/journal.pone.0272009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
During the COVID-19 pandemic, several countries have resorted to self-adaptive mechanisms that tailor non-pharmaceutical interventions to local epidemiological and health care indicators. These mechanisms reinforce the mutual influence between containment measures and the evolution of the epidemic. To account for such interplay, we develop an epidemiological model that embeds an algorithm mimicking the self-adaptive policy mechanism effective in Italy between November 2020 and March 2022. This extension is key to tracking the historical evolution of health outcomes and restrictions in Italy. Focusing on the epidemic wave that started in mid-2021 after the diffusion of Delta, we compare the functioning of alternative mechanisms to show how the policy framework may affect the trade-off between health outcomes and the restrictiveness of mitigation measures. Mechanisms based on the reproduction number are generally highly responsive to early signs of a surging wave but entail severe restrictions. The emerging trade-off varies considerably depending on specific conditions (e.g., vaccination coverage), with less-reactive mechanisms (e.g., those based on occupancy rates) becoming more appealing in favorable contexts.
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Affiliation(s)
- Sabina Marchetti
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Alessandro Borin
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | | | - Giuseppe Ilardi
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Antonino Bella
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Paola Stefanelli
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Flavia Riccardo
- Department of Infectious Diseases, Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Andrea Brandolini
- Directorate General for Economics, Statistics and Research, Bank of Italy, Rome, Italy
| | - Silvio Brusaferro
- Italian National Institute of Health (Istituto Superiore di Sanità), Rome, Italy
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Conteduca FP, Borin A. A New Dataset for Local and National COVID-19-Related Restrictions in Italy. Ital Econ J 2022; 8:435-470. [PMCID: PMC9163925 DOI: 10.1007/s40797-022-00197-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/21/2022] [Indexed: 12/29/2023]
Abstract
This paper presents a novel dataset of non-pharmaceutical interventions adopted by Italian authorities to tackle the COVID-19 pandemic at the national and local levels. The dataset follows the structure of the Oxford Coronavirus Government Response Tracker (OxCGRT; Hale et al. in Nat Human Behav 5:529–538, 10.1038/s41562-021-01079-8, 2021)). We include several novelties with respect to the original source. First, we tailor the classification of provisions to the measures adopted in Italy. Second, we collect detailed information on local restrictions in the country, including lockdowns and school closures. Third, we apply a bottom-up approach to construct population-weighted average stringency indexes (Italian Stringency Indexes, ItSIs) at the provincial, regional, and country-wide levels. While expanding the geographical coverage of the stringency indicators, we preserve the comparability of the ItSIs with the original stringency index published in the OxCGRT. As an application, we show that the correlations of our ItSI with community mobility indicators and various measures of economic activity are higher than those obtained with the OxCGRT indicator.
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