Dataset Information

Community movement and COVID-19: a global study using Google's Community Mobility Reports.

ABSTRACT: Google's 'Community Mobility Reports' (CMR) detail changes in activity and mobility occurring in response to COVID-19. They thus offer the unique opportunity to examine the relationship between mobility and disease incidence. The objective was to examine whether an association between COVID-19-confirmed case numbers and levels of mobility was apparent, and if so then to examine whether such data enhance disease modelling and prediction. CMR data for countries worldwide were cross-correlated with corresponding COVID-19-confirmed case numbers. Models were fitted to explain case numbers of each country's epidemic. Models using numerical date, contemporaneous and distributed lag CMR data were contrasted using Bayesian Information Criteria. Noticeable were negative correlations between CMR data and case incidence for prominent industrialised countries of Western Europe and the North Americas. Continent-wide examination found a negative correlation for all continents with the exception of South America. When modelling, CMR-expanded models proved superior to the model without CMR. The predictions made with the distributed lag model significantly outperformed all other models. The observed relationship between CMR data and case incidence, and its ability to enhance model quality and prediction suggests data related to community mobility could prove of use in future COVID-19 modelling.

SUBMITTER: Sulyok M

PROVIDER: S-EPMC7729173 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Community movement and COVID-19: a global study using Google's Community Mobility Reports.

Sulyok M M Walker M M

Epidemiology and infection 20201113

Google's 'Community Mobility Reports' (CMR) detail changes in activity and mobility occurring in response to COVID-19. They thus offer the unique opportunity to examine the relationship between mobility and disease incidence. The objective was to examine whether an association between COVID-19-confirmed case numbers and levels of mobility was apparent, and if so then to examine whether such data enhance disease modelling and prediction. CMR data for countries worldwide were cross-correlated with ...[more]

PMID: 33183366

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Project description:Background:Coronavirus 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. Objective:To 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 & Methods:This 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. Results:Across 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. Conclusion:Study 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.

Project description:To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62 and -13.77% for container ships, between +2.28 and -3.32% for dry bulk, between -0.22 and -9.27% for wet bulk, and between -19.57 and -42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

Dataset Information

Community movement and COVID-19: a global study using Google's Community Mobility Reports.

Publications

Community movement and COVID-19: a global study using Google's Community Mobility Reports.

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