Periyasamy Aravind Gandhi, Venkatesh U
Department of Community Medicine, School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Department of Community Medicine, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Healthc Inform Res. 2021 Oct;27(4):325-334. doi: 10.4258/hir.2021.27.4.325. Epub 2021 Oct 31.
Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategy to enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of the lockdown measures taken in the world's second-most populous country, India, by exploring their relationship with community mobility patterns and the doubling time of COVID-19.
We conducted a retrospective analysis based on community mobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in India between February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, community mobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict the doubling time of COVID-19.
Community mobility drastically fell after the lockdown was instituted. The doubling time 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 mobility patterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model's predictive power increased when the stringency index was also added (R2 = 0.73).
Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system's capacity to manage.
物理距离是预防2019冠状病毒病（COVID-19）的一项控制措施。封锁是在城市地区强制实施物理距离的一种策略，但它们是严厉的措施。因此，我们通过探索印度（世界第二人口大国）所采取的封锁措施与社区流动模式以及COVID-19病例倍增时间之间的关系，评估了这些措施的有效性。
我们基于2020年2月15日至4月26日期间印度的社区流动模式、封锁措施的严格指数以及COVID-19病例的倍增时间进行了回顾性分析。计算了严格指数、社区流动模式与COVID-19病例倍增时间之间的Pearson相关系数。应用多元线性回归来预测COVID-19的倍增时间。
实施封锁后，社区流动大幅下降。COVID-19病例的倍增时间与户外区域的人口流动模式呈负相关（r = -0.45至-0.58）。严格指数与户外流动模式也呈负相关（r = -0.89至-0.95）。发现人口流动模式（R2 = 0.67）可预测COVID-19的倍增时间，当加入严格指数时，模型的预测能力增强（R2 = 0.73）。
封锁措施可有效确保印度的物理距离并减少短期病例激增。因此，每当预计COVID-19病例将超过医疗系统的管理能力时，可考虑间歇性地量身定制实施封锁措施。
