Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis.
JMIR Public Health Surveill
; 7(8): e29957, 2021 Aug 30.
Article
in English
| MEDLINE | ID: covidwho-2141339
ABSTRACT
BACKGROUND:
Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.OBJECTIVE:
The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.METHODS:
In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.RESULTS:
At the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.CONCLUSIONS:
Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Travel
/
Cell Phone
/
Geographic Information Systems
/
Pandemics
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
JMIR Public Health Surveill
Year:
2021
Document Type:
Article
Affiliation country:
29957
Similar
MEDLINE
...
LILACS
LIS