Literature DB >> 32426117

Novel approaches to estimate compliance with lockdown measures in the COVID-19 pandemic.

Asiyah Sheikh1, Zakariya Sheikh1, Aziz Sheikh2.   

Abstract

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Year:  2020        PMID: 32426117      PMCID: PMC7211415          DOI: 10.7189/jogh.10.010348

Source DB:  PubMed          Journal:  J Glob Health        ISSN: 2047-2978            Impact factor:   4.413


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A lockdown is a social distancing intervention that aims to minimise physical contact between individuals and groups in order to reduce transmission of a communicable disease [1]. Social distancing measures are typically introduced in an attempt to reduce and/or delay the peak of an epidemic/pandemic, to minimise the potential for surges in health care utilisation and to protect vulnerable groups. In the context of COVID-19, the World Health Organization has encouraged use of the term ‘physical distancing’ instead of social distancing to highlight that the aim of this intervention is only to reduce physical contact, not social contact which is often still possible through telephone and video calls, and social media [2]. There are a range of physical distancing measures, which can be broadly categorised as operating at the individual (eg, to support self-isolation of confirmed or suspected cases) or population levels (eg, closing of schools or workplaces) [1]. The lack of any effective pharmaceutical interventions for COVID-19 or vaccine against SARS-CoV-2 and the resulting failure to contain the virus has led to widespread adoption of physical distancing measures [3]. For example, according to the United Nations Educational, Scientific and Cultural Organization (UNESCO), 188 (96%) countries have by April 2020 implemented nationwide closure of schools [4]. Estimating compliance of policies encouraging physical distancing is crucial to assess their effectiveness and for accurate modelling of the future course of the pandemic. This viewpoint highlights three novel approaches that are now being deployed in some countries to assess compliance with physical distancing measures in the context of the COVID-19 pandemic. It is our hope that, by drawing attention to these measures, other parts of the world may also consider, where feasible and appropriate, deploying similar approaches.

NOVEL APPROACHES TO ESTIMATING PHYSICAL DISTANCING AND COMPLIANCE WITH LOCKDOWN

Global positioning system (GPS) data provided by mobile phone carriers

A number of governments have been provided anonymous aggregated mobile phone GPS data allowing monitoring of compliance with physical distancing at the population level (eg, Austria, Germany and Italy) [5]. In contrast, other countries are using more invasive techniques, using data on individuals, to assess compliance with physical distancing measures, trace contacts and enforce quarantine orders (eg, China, South Korea, Taiwan) [5].

Mobile phone GPS data provided by other technology companies

A number of other technology companies (eg, IBM, Uber) are also sharing GPS data with academic institutions, governments and health providers (eg, Facebook Disease Prevention Maps have been shared with the London School of Hygiene & Tropical Medicine, which has played a major role in developing COVID-19 models for the UK) [6]. Recently, Google has published community mobility reports containing information on changes in mobility for six categories of location (eg, retail & recreation, parks and home) for 131 countries at national and local levels, which will be regularly updated [7]. The UK Government is using these data source as well as data on transport use to inform decision making, and have shared some of these in a recent press conference [8]. Similarly, Unacast has repurposed location and movement data that they collect via mobile phones to create a daily updated interactive scoreboard for “social distancing activity” for the United States that gives data at national, state and county levels () [9]. As this data is hyperlocal it allows comparisons of the effectiveness of physical distancing policies to be drawn, which can prove useful in the context of shaping regional and national public health messaging.
Figure 1

Screenshot of Unacast Social Distancing Scoreboard as of the 22nd of April 2020 [9].

Screenshot of Unacast Social Distancing Scoreboard as of the 22nd of April 2020 [9].

Traffic congestion and public transport usage

Alternative proxy measurements for physical distancing compliance include use of data on traffic congestion and public transport, which are often routinely collected through various means (eg, mobile phone GPS, road sensors). For example, the Inter-American Development Bank and IDB Invest have developed a public COVID-19 dashboard for Latin American and Caribbean countries including traffic data supplied by the navigation apps, Waze & Moovit () [10,11]. Similarly, CityMapper has created a mobility index by comparing typical and current usage of its navigation app [12].
Figure 2

Screenshot of part of Inter-American Development Bank and IDB Invest Coronavirus Impact Dashboard as of the 22nd April 2020 [10].

Screenshot of part of Inter-American Development Bank and IDB Invest Coronavirus Impact Dashboard as of the 22nd April 2020 [10]. Photo: From the collection of Mateusz Glogowski (used with permission).

CONCLUSIONS

COVID-19 has resulted in the introduction of lockdowns on an unprecedented scale in an attempt to modify the course of the pandemic and mitigate its effects. Measuring compliance with physical distancing is crucial to inform modelling deliberations and enable evidence-based policy making. We have summarised three key approaches that are currently being used to help estimate compliance with lockdowns in the context of the COVID-19 pandemic. Although currently used in a small minority of countries, there is substantial opportunity to scale up use of these approaches globally.
  1 in total

1.  Scientific and ethical basis for social-distancing interventions against COVID-19.

Authors:  Joseph A Lewnard; Nathan C Lo
Journal:  Lancet Infect Dis       Date:  2020-03-23       Impact factor: 25.071

  1 in total
  10 in total

1.  Political orientation, moral foundations, and COVID-19 social distancing.

Authors:  Hammond Tarry; Valérie Vézina; Jacob Bailey; Leah Lopes
Journal:  PLoS One       Date:  2022-06-24       Impact factor: 3.752

2.  Bidirectional imputation of spatial GPS trajectories with missingness using sparse online Gaussian Process.

Authors:  Gang Liu; Jukka-Pekka Onnela
Journal:  J Am Med Inform Assoc       Date:  2021-07-30       Impact factor: 7.942

3.  Public Compliance With Social Distancing Measures and SARS-CoV-2 Spread : A Quantitative Analysis of 5 States.

Authors:  Hongjie Liu; Chang Chen; Raul Cruz-Cano; Jennifer L Guida; Minha Lee
Journal:  Public Health Rep       Date:  2021-04-28       Impact factor: 3.117

4.  The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: A systematic review and meta-analysis.

Authors:  Jiawen Deng; Fangwen Zhou; Wenteng Hou; Zachary Silver; Chi Yi Wong; Oswin Chang; Anastasia Drakos; Qi Kang Zuo; Emma Huang
Journal:  Psychiatry Res       Date:  2021-03-09       Impact factor: 11.225

5.  Association of Social Distancing, Population Density, and Temperature With the Instantaneous Reproduction Number of SARS-CoV-2 in Counties Across the United States.

Authors:  David Rubin; Jing Huang; Brian T Fisher; Antonio Gasparrini; Vicky Tam; Lihai Song; Xi Wang; Jason Kaufman; Kate Fitzpatrick; Arushi Jain; Heather Griffis; Koby Crammer; Jeffrey Morris; Gregory Tasian
Journal:  JAMA Netw Open       Date:  2020-07-01

6.  County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model.

Authors:  Kevin L McKee; Ian C Crandell; Alexandra L Hanlon
Journal:  JMIR Public Health Surveill       Date:  2020-12-23

7.  Household visitation during the COVID-19 pandemic.

Authors:  Stuart Ross; George Breckenridge; Mengdie Zhuang; Ed Manley
Journal:  Sci Rep       Date:  2021-11-25       Impact factor: 4.379

8.  Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking.

Authors:  Marco Vinceti; Tommaso Filippini; Kenneth J Rothman; Fabrizio Ferrari; Alessia Goffi; Giuseppe Maffeis; Nicola Orsini
Journal:  EClinicalMedicine       Date:  2020-07-13

Review 9.  Reopening safely - Lessons from Taiwan's COVID-19 response.

Authors:  Cheryl Lin; Jewel Mullen; Wendy E Braund; Pikuei Tu; John Auerbach
Journal:  J Glob Health       Date:  2020-12       Impact factor: 4.413

10.  Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020.

Authors:  Matia Vannoni; Martin McKee; Jan C Semenza; Chris Bonell; David Stuckler
Journal:  Global Health       Date:  2020-09-23       Impact factor: 4.185

  10 in total

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