Healthc Inform Res.  2022 Apr;28(2):160-169. 10.4258/hir.2022.28.2.160.

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

• Venkatesh U
• Gandhi P A
• Ara T
• Rahman MM
• Kishore J

• KMID: 2529798
• DOI: http://doi.org/10.4258/hir.2022.28.2.160

Abstract

Objectives
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.
Methods
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.
Results
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.
Conclusions
COVID-19 lockdowns achieved the immediate objective of mobility reduction in countries with a high burden of cases.

Keyword

COVID-19; Spatio-Temporal Analysis; Geographic Information Systems; Information Technology; Infectious Disease Transmission

Figure

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  Figure 1 Mean changes in mobility patterns of the selected countries.

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  Figure 2 Clusters of average mobility reductions: results from k-means clustering. (A) Optimal number of clusters. (B) Clusters.

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  Figure 3 Percentages of variation explained by the first principal component.

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  Figure 4 Associations between mobility and COVID-19 case doubling time: results from finite distributed lag models.

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  Figure 5 Normal Q-Q plots of residuals.

Reference

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