Abstract

The implementation of governmental Non-Pharmaceutical Interventions (NPIs) has been the primary means of controlling the spread of the COVID-19 disease. One of the intended effects of these NPIs has been to reduce population mobility. Due to the huge costs of implementing these NPIs, it is essential to have a good understanding of their efficacy. Using aggregated mobility data per country, released by Apple and Google we investigated the proportional contribution of NPIs to the magnitude and rate of mobility changes at a multi-national level. NPIs with the greatest impact on the magnitude of mobility change were lockdown measures; declaring a state of emergency; closure of businesses and public services and school closures. NPIs with the greatest effect on the rate of mobility change were implementation of lockdown measures and limitation of public gatherings. As confirmed by chi-square and cluster analysis, separately recorded NPIs like school closure and closure of businesses and public services were closely correlated with each other, both in timing and occurrence. This suggests that the observed significant NPI effects are mixed with and amplified by their correlated NPI measures. We observed direct and similar effects of NPIs on both Apple and Google mobility data. In addition, although Apple and Google data were obtained by different methods they were strongly correlated indicating that they are reflecting overall mobility on a country level. The availability of this data provides an opportunity for governments to build timely, uniform and cost-effective mechanisms to monitor COVID-19 or future pandemic countermeasures.

Details

Title
Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data
Author
Snoeijer, Berber T 1   VIAFID ORCID Logo  ; Burger Mariska 2   VIAFID ORCID Logo  ; Sun, Shaoxiong 3   VIAFID ORCID Logo  ; Dobson Richard J B 4 ; Folarin, Amos A 5   VIAFID ORCID Logo 

1  ClinLine, Leiderdorp, The Netherlands 
2  OCS Life Sciences, ‘s Hertogenbosch, The Netherlands 
3  The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
4  The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); University College London, Institute of Health Informatics, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201) 
5  The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); University College London, Institute of Health Informatics, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); South London and Maudsley NHS Foundation Trust, London, UK (GRID:grid.37640.36) (ISNI:0000 0000 9439 0839) 
Publication year
2021
Publication date
Dec 2021
Publisher
Nature Publishing Group
e-ISSN
23986352
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41746-021-00451-2
ProQuest document ID
2529244490
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.