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Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries.

Authors
  • Venkatesh, U1
  • Gandhi P, Aravind2
  • Ara, Tasnim3
  • Rahman, Md Mahabubur3
  • Kishore, Jugal4
  • 1 Department of Community Medicine & Family Medicine, All India Institute of Medical Sciences, Gorakhpur, India. , (India)
  • 2 Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India. , (India)
  • 3 Institute of Statistical Research & Training, University of Dhaka, Dhaka, Bangladesh. , (Bangladesh)
  • 4 Department of Community Medicine, Vardhman Mahavir Medical College & Safdarjung Hospital, New Delhi, India. , (India)
Type
Published Article
Journal
Healthcare informatics research
Publication Date
Apr 01, 2022
Volume
28
Issue
2
Pages
160–169
Identifiers
DOI: 10.4258/hir.2022.28.2.160
PMID: 35576984
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

During the coronavirus disease 2019 (COVID-19) pandemic, countries around the world framed specific laws and imposed varying degrees of lockdowns to ensure the maintenance of physical distancing. Understanding changes in temporal and spatial mobility patterns may provide insights into the dynamics of this infectious disease. Therefore, we assessed the efficacy of lockdown measures in 16 countries worldwide by analyzing the relationship between community mobility patterns and the doubling time of COVID-19. We performed a retrospective record-based analysis of population-level data on the doubling time for COVID-19 and community mobility. The doubling time for COVID-19 was calculated based on the laboratory-confirmed cases reported daily over the study period (from February 15 to May 2, 2020). Principal component analysis (PCA) of six mobility pattern-related variables was conducted. To explain the magnitude of the effect of mobility on the doubling time, a finite linear distributed lag model was fitted. The k-means clustering approach was employed to identify countries with similar patterns in the significant co-efficient of the mobility index, with the optimal number of clusters derived using Elbow's method. The countries analyzed had reduced mobility in commercial and social places. Reduced mobility had a significant and favorable association with the doubling time of COVID-19-specifically, the greater the mobility reduction, the longer the time taken for the COVID-19 cases to double. COVID-19 lockdowns achieved the immediate objective of mobility reduction in countries with a high burden of cases.

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