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Introduction <p>Changes in community mobility—especially movement through public-facing settings were closely linked to COVID-19 spread during India’s first pandemic year. Quantifying the relationship between mobility restrictions and subsequent changes in reported cases is crucial for designing effective non-pharmaceutical interventions in future outbreaks.</p> Methods <p>We analyzed state/UT (Union Territory)-level daily confirmed COVID-19 cases from March 14, 2020, to December 31, 2020, and Google Community Mobility indicators for retail &amp; recreation, grocery &amp; pharmacy, parks, transit stations, workplaces, and residential activity. To accommodate over-dispersion in count outcomes, we fitted Negative Binomial generalized linear models with a uniform 14-day mobility lag to reflect incubation and reporting delays. Model adequacy was evaluated using residual diagnostics and Q–Q plots. Sector effects were summarized using Absolute Attributable Cases (AAC Final), expressed in cases/day.</p> Results <p>Reduced mobility in retail &amp; recreation and transit stations yielded the most consistently negative AAC Final values (greater reductions), indicating the largest model-implied decreases in daily cases, typically tens of cases/day and exceeding 100 cases/day in some higher-burden states. Parks and workplaces showed smaller and more region-specific associations. In contrast, increased residential mobility was generally associated with positive AAC Final values.</p> Conclusion <p>Decreased mobility in public-facing settings, particularly retail/recreation and transit, was most strongly associated with subsequent declines in reported daily cases, supporting targeted mobility control as a practical outbreak-mitigation lever.</p>

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Human mobility patterns and COVID-19 trends in India an evidence based framework for targeted interventions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Subrahmanya Hari Prasad Peri,
  • Meghana Veera,
  • Bhanu Varsha Veera

摘要

Introduction

Changes in community mobility—especially movement through public-facing settings were closely linked to COVID-19 spread during India’s first pandemic year. Quantifying the relationship between mobility restrictions and subsequent changes in reported cases is crucial for designing effective non-pharmaceutical interventions in future outbreaks.

Methods

We analyzed state/UT (Union Territory)-level daily confirmed COVID-19 cases from March 14, 2020, to December 31, 2020, and Google Community Mobility indicators for retail & recreation, grocery & pharmacy, parks, transit stations, workplaces, and residential activity. To accommodate over-dispersion in count outcomes, we fitted Negative Binomial generalized linear models with a uniform 14-day mobility lag to reflect incubation and reporting delays. Model adequacy was evaluated using residual diagnostics and Q–Q plots. Sector effects were summarized using Absolute Attributable Cases (AAC Final), expressed in cases/day.

Results

Reduced mobility in retail & recreation and transit stations yielded the most consistently negative AAC Final values (greater reductions), indicating the largest model-implied decreases in daily cases, typically tens of cases/day and exceeding 100 cases/day in some higher-burden states. Parks and workplaces showed smaller and more region-specific associations. In contrast, increased residential mobility was generally associated with positive AAC Final values.

Conclusion

Decreased mobility in public-facing settings, particularly retail/recreation and transit, was most strongly associated with subsequent declines in reported daily cases, supporting targeted mobility control as a practical outbreak-mitigation lever.