ArticleJMIR public health and surveillance2021
Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Health beliefs and their relation to household, necessary, and unnecessary contacts across COVID-19 epidemic waves in Taiwan.BMC public health · 2025Article
- Investigating Google Trends to forecast acute febrile illness outbreaks in North India reported through the Integrated Disease Surveillance Program.BMC infectious diseases · 2025Article
- Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.The Indian journal of medical research · 2025Article
- Impact of Social Restrictions During COVID-19 on the Aquatic Levels of Antimicrobials and Other Drugs in Delhi.Cureus · 2024Article
- Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.JMIR public health and surveillance · 2024Article
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021.Preventive medicine reports · 2023Article
- COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?Healthcare (Basel, Switzerland) · 2023Article
- The lockdown and vaccination distribution in Thailand's COVID-19 epidemic: A model study.Infectious Disease Modelling · 2023Article
- Impact of Human Mobility on COVID-19 Transmission According to Mobility Distance, Location, and Demographic Factors in the Greater Bay Area of China: Population-Based Study.JMIR public health and surveillance · 2023Article
- Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study.JMIR public health and surveillance · 2023Article
- Changes in mobility patterns during the COVID-19 pandemic in Zambia: Implications for the effectiveness of NPIs in Sub-Saharan Africa.PLOS global public health · 2023Article
- Beyond SARS-CoV-2: epidemiological surveillance of respiratory viruses in Jalisco, Mexico.Frontiers in public health · 2023Article
- Article
- Client satisfaction with telemedicine services during COVID-19 pandemic: A cross-sectional survey from a teaching institute of North India.Journal of family medicine and primary care · 2022Article
- Mask wearing in community settings reduces SARS-CoV-2 transmission.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Observational
- Characterizing COVID-19 waves in urban and rural districts of India.npj urban sustainability · 2022Article
- It is complicated: Potential short- and long-term impact of coronavirus disease 2019 (COVID-19) on antimicrobial resistance-An expert review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2022Review
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4 authors.
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Abstract
backgroundAssociation 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.
objectiveThe aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.
methodsIn 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.
resultsAt 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.
conclusionsMobile-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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