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Lockdowns & Rebounds: A Data Analysis of What Happens Next

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Barry SmythAuthors Info & Claims

Digital Government: Research and Practice, Volume 1, Issue 4

Article No.: 27, Pages 1 - 7

https://doi.org/10.1145/3411762

Published: 17 August 2020 Publication History

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Abstract

We present an analysis of COVID-19 lockdowns for 56 countries based on daily mobility levels and estimated virus transmission rates. What will happen to transmission rates when countries begin to ease restrictions and mobility levels start to increase? Our analysis, based on data up to May 11th, 2020, show how, even when mobility levels increase, most countries (87%) continue to reduce the effective reproduction number (R_t) of the virus. Moreover, when restrictions were eased only after R_t was below 1, then countries always managed to maintain R_t≤ 1 even as mobility levels rose, at least for the period covered by the data. In contrast, when restrictions were eased before R_t fell below 1, then only 55% of countries managed to subsequently reduce R_t to below 1. By easing restrictions cautiously and carefully, it was possible to avoid a rebound in transmission rates while allowing mobility levels to increase.

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Cited By

Smyth B(2022)The fading impact of lockdowns: A data analysis of the effectiveness of Covid-19 travel restrictions during different pandemic phasesPLOS ONE10.1371/journal.pone.026977417:6(e0269774)Online publication date: 17-Jun-2022
https://doi.org/10.1371/journal.pone.0269774
Gao YJanssen M(2022)The Open Data Canvas–Analyzing Value Creation from Open DataDigital Government: Research and Practice10.1145/35111023:1(1-15)Online publication date: 26-Mar-2022
https://dl.acm.org/doi/10.1145/3511102
Smyth B(2021)Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 PandemicFrontiers in Digital Health10.3389/fdgth.2021.6557453Online publication date: 28-May-2021
https://doi.org/10.3389/fdgth.2021.655745

Index Terms

Lockdowns & Rebounds: A Data Analysis of What Happens Next
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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Published In

cover image Digital Government: Research and Practice

Digital Government: Research and Practice Volume 1, Issue 4

COVID-19 Commentaries

October 2020

75 pages

EISSN:2639-0175

DOI:10.1145/3411770

Editors:
Soon Ae Chun
City University of New York, USA
,
Beth Simone Noveck
The Governance Lab and New York University, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2020

Accepted: 01 July 2020

Revised: 01 July 2020

Received: 01 June 2020

Published in DGOV Volume 1, Issue 4

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Cited By

Smyth B(2022)The fading impact of lockdowns: A data analysis of the effectiveness of Covid-19 travel restrictions during different pandemic phasesPLOS ONE10.1371/journal.pone.026977417:6(e0269774)Online publication date: 17-Jun-2022
https://doi.org/10.1371/journal.pone.0269774
Gao YJanssen M(2022)The Open Data Canvas–Analyzing Value Creation from Open DataDigital Government: Research and Practice10.1145/35111023:1(1-15)Online publication date: 26-Mar-2022
https://dl.acm.org/doi/10.1145/3511102
Smyth B(2021)Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 PandemicFrontiers in Digital Health10.3389/fdgth.2021.6557453Online publication date: 28-May-2021
https://doi.org/10.3389/fdgth.2021.655745

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