Population Mobility, Lockdowns, and COVID-19 Control: An Analysis Based on Google Location Data and Doubling Time from India
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Objectives: Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategyto enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of thelockdown measures taken in the world’s second-most populous country, India, by exploring their relationship with communitymobility patterns and the doubling time of COVID-19. Methods: We conducted a retrospective analysis based on communitymobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in Indiabetween February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, communitymobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict thedoubling time of COVID-19. Results: Community mobility drastically fell after the lockdown was instituted. The doublingtime of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = –0.45 to –0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = –0.89 to –0.95). Population mobilitypatterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model’s predictive power increased whenthe stringency index was also added (R2 = 0.73). Conclusions: Lockdown measures could effectively ensure physical distancingand reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementationon an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system’s capacity to manage.
Abstract
Objectives: Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategyto enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of thelockdown measures taken in the world’s second-most populous country, India, by exploring their relationship with communitymobility patterns and the doubling time of COVID-19. Methods: We conducted a retrospective analysis based on communitymobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in Indiabetween February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, communitymobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict thedoubling time of COVID-19. Results: Community mobility drastically fell after the lockdown was instituted. The doublingtime of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = –0.45 to –0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = –0.89 to –0.95). Population mobilitypatterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model’s predictive power increased whenthe stringency index was also added (R2 = 0.73). Conclusions: Lockdown measures could effectively ensure physical distancingand reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementationon an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system’s capacity to manage.
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