Movement and risk perception: evidence from spatial analysis of mobile phone-based mobility during the COVID-19 lockdown, Nigeria

Lawal, Olanrewaju; Nwegbu, Chidozie

doi:10.1007/s10708-020-10331-z

Cited by 20 publications

(19 citation statements)

References 28 publications

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“…Of the 14 included studies, four studies included data from multiple countries across Europe, North America, Asia, Australasia and Africa [ 24 , 28 , 30 , 32 ]. Four studies were conducted in the United States of America (USA) [ 19 , 21 , 23 , 25 ] and other studies used data from Australia [ 31 ], Canada [ 26 ], India [ 29 ], Nigeria [ 27 ], South Africa [ 20 ] and Turkey [ 22 ] ( Table 1 ). All studies reported data from the first wave of the pandemic, with Google Mobility Scores included from January to August 2020, with most studies analysing data during the first two months of the pandemic.…”

Section: Resultsmentioning

confidence: 99%

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The effect of different COVID-19 public health restrictions on mobility: A systematic review

et al. 2021

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Background In response to the COVID-19 pandemic, most countries have introduced non-pharmaceutical interventions, such as stay-at-home orders, to reduce person-to-person contact and break trains of transmission. The aim of this systematic review was to assess the effect of different public health restrictions on mobility across different countries and cultures. The University of Bern COVID-19 Living Evidence database of COVID-19 and SARS-COV-2 publications was searched for retrospective or prospective studies evaluating the impact of COVID-19 public health restrictions on Google Mobility. Titles and abstracts were independently screened by two authors. Information from included studies was extracted by one researcher and double checked by another. Risk of bias of included articles was assessed using the Newcastle Ottowa Scale. Given the heterogeneous nature of the designs used, a narrative synthesis was undertaken. From the search, 1672 references were identified, of which 14 were included in the narrative synthesis. All studies reported data from the first wave of the pandemic, with Google Mobility Scores included from January to August 2020, with most studies analysing data during the first two months of the pandemic. Seven studies were assessed as having a moderate risk of bias and seven as a low risk of bias. Countries that introduced more stringent public health restrictions experienced greater reductions in mobility, through increased time at home and reductions in visits to shops, workplaces and use of public transport. Stay-at-home orders were the most effective of the individual strategies, whereas mask mandates had little effect of mobility. Conclusions Public health restrictions, particularly stay-at-home orders have significantly impacted on transmission prevention behaviours. Further research is required to understand how to effectively address pandemic fatigue and to support the safe return back to normal day-to-day behaviours.

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Section: Resultsmentioning

confidence: 99%

“…Changes across most or all of the Google Mobility variables where reported separately by 9 of the 14 studies [ 19 – 21 , 25 , 27 , 29 – 32 ], whereas four studies only reported aggregated scores score [ 22 , 23 , 26 , 28 ] or just assessed changes in one variable [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

The effect of different COVID-19 public health restrictions on mobility: A systematic review

et al. 2021

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“…Ropkins and Tate [30] used data from several stations across the UK to examine the effect of lockdown on air pollutants while they used the backgrounds of the stations (Rural, Urban, and Traffic) to investigate the effect of traffic. Some studies have successfully used geolocation mobility data provided by Google and Apple as proxies of traffic to investigate movement and risk perception [35] and changes in air quality [36,37] during COVID-19 lockdown. Munir et al [38] in a study in Sheffield, UK highlighted the role of traffic in driving NO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

et al. 2021

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The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

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“…The Google mobility data on stay-at-home activities reveals the level of public social distancing behaviour during the COVID-19 pandemic (e.g., Wang and Yamamoto, 2020;Noland, 2021;Yilmazkuday, 2021). Prior studies have suggested that an individual's mobility information reflects the degree to which his or her mobility is restricted during the pandemic; in our case, it captures individuals' risk perception during the epidemic (e.g., Kielland, 2016;Hsiehchen et al, 2020;Lawal and Nwegbu, 2020). After merging the mobility data with the demographic and COVID crisis data based on Federal Information Processing Standards, our final sample contains 1,776 counties.…”

Section: Introductionmentioning

confidence: 89%

Please Let Science Talk: Presidential Sentiment and the Public’s Risk Perception in the Era of COVID-19

Guo

Yang

Zeng

et al. 2021

Preprint

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We examine whether and to what extent the presidential COVID-19 sentiment (PCS), constructed based on the former President Trump’s tweets, has influenced the public’s risk perception regarding COVID-19. We find that during the COVID-19 pandemic, PCS is negatively associated with social distancing behaviour in the Republican counties but is positively associated with social distancing behaviour in the Democratic counties. Our finding supports the view that individuals’ risk perception is influenced by the information they receive from politicians through social media, with the impact exhibiting as variations with respect to the individuals’ position on the political spectrum.

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Movement and risk perception: evidence from spatial analysis of mobile phone-based mobility during the COVID-19 lockdown, Nigeria

Cited by 20 publications

References 28 publications

The effect of different COVID-19 public health restrictions on mobility: A systematic review

The effect of different COVID-19 public health restrictions on mobility: A systematic review

Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

Please Let Science Talk: Presidential Sentiment and the Public’s Risk Perception in the Era of COVID-19

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