AI Article Synopsis
- The study examines how human mobility, as measured by Google’s Community Mobility Reports (CMRs), influenced the spread of COVID-19 in India during different phases of lockdown.
- It utilized mobility data from March to October 2020, analyzing changes before, during, and after lockdowns, comparing them to earlier periods, and looking for correlations between mobility and COVID-19 severity.
- Results showed a significant decrease in mobility during lockdowns, with notable differences across states, and a weak correlation between residential mobility and overall mobility indicators, especially during the lockdown phase.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407437 | PMC |
| http://dx.doi.org/10.2196/29957 | DOI Listing |
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