Dataset Information

Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries.

ABSTRACT:

Objectives

During the coronavirus disease 2019 (COVID-19) pandemic, countries around the world framed specific laws and imposed varying degrees of lockdowns to ensure the maintenance of physical distancing. Understanding changes in temporal and spatial mobility patterns may provide insights into the dynamics of this infectious disease. Therefore, we assessed the efficacy of lockdown measures in 16 countries worldwide by analyzing the relationship between community mobility patterns and the doubling time of COVID-19.

Methods

We performed a retrospective record-based analysis of population-level data on the doubling time for COVID-19 and community mobility. The doubling time for COVID-19 was calculated based on the laboratory-confirmed cases reported daily over the study period (from February 15 to May 2, 2020). Principal component analysis (PCA) of six mobility pattern-related variables was conducted. To explain the magnitude of the effect of mobility on the doubling time, a finite linear distributed lag model was fitted. The k-means clustering approach was employed to identify countries with similar patterns in the significant co-efficient of the mobility index, with the optimal number of clusters derived using Elbow's method.

Results

The countries analyzed had reduced mobility in commercial and social places. Reduced mobility had a significant and favorable association with the doubling time of COVID-19-specifically, the greater the mobility reduction, the longer the time taken for the COVID-19 cases to double.

Conclusions

COVID-19 lockdowns achieved the immediate objective of mobility reduction in countries with a high burden of cases.

SUBMITTER: Venkatesh U

PROVIDER: S-EPMC9117801 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries.

Venkatesh U U Gandhi P Aravind A Ara Tasnim T Rahman Md Mahabubur MM Kishore Jugal J

Healthcare informatics research 20220430 2

<h4>Objectives</h4>During the coronavirus disease 2019 (COVID-19) pandemic, countries around the world framed specific laws and imposed varying degrees of lockdowns to ensure the maintenance of physical distancing. Understanding changes in temporal and spatial mobility patterns may provide insights into the dynamics of this infectious disease. Therefore, we assessed the efficacy of lockdown measures in 16 countries worldwide by analyzing the relationship between community mobility patterns and t ...[more]

PMID: 35576984

Similar Datasets

Project description:BackgroundOn January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries.ObjectiveThe first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space-time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide.MethodsWe created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space-time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used.ResultsWe found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread.ConclusionsThe prospective space-time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.

Project description:BackgroundCoronavirus disease (COVID-19) is a novel public health problem threatening the whole world. As an upshot, countrywide lockdown due to COVID-19 pandemic has been supportive of changing community mobility trends of various place categories including retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential in India.ObjectiveTo analyze the impact of lockdown for COVID-19 on community mobility using spatial time-series change over different states and union territories (UTs) of India.Data & methodsThis study has been organized based on states & UTs wise time-series data of the daily percentage of change of community mobility from baseline in India, collected from 15th February to 30th April 2020. Conditional formatting techniques, time-series trends plotting method, spatial inverse distance weighted (IDW) interpolation mapping techniques have been employed to show pre and post lockdown mobility trends due to COVID-19 i.e. to fulfill the objective.ResultsAcross India, retail and recreation, grocery and pharmacy, visits to parks, transit stations, and workplaces mobility dropped by -73.4%, -51.2%, -46.3%, -66% and -56.7% respectively. Visits to residential places mobility increased by 23.8% as people mostly stayed home during the lockdown. The COVID-19 lockdown started on 24 March 2020 and just gone one day (March 25, 2020) of the beginning of lockdown, there have a decreased in percentage (-70.51% in retail and recreation mobility), (-64.26% in grocery and pharmacy mobility), (-46.17% in parks mobility), (-65.6% in transit stations mobility), (-60.03% in workplaces mobility) from baseline in compared to the pre-lockdown period and residential mobility has been increased in percentage (26.32%) from baseline due to people stayed home during the lockdown for COVID-19 pandemic in India.ConclusionStudy figures out mobility trends over time during pre-lockdown and after lockdown period across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential, which can be used in public health strategies to drop the spread of COVID-19.

Dataset Information

Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries.

Objectives

Methods

Results

Conclusions

Publications

Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets