Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data
| Autor: | Berber T. Snoeijer, Mariska Burger, Shaoxiong Sun, Amos Folarin, Richard Dobson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Geographic mobility Physics - Physics and Society 020205 medical informatics Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Computer applications to medicine. Medical informatics Psychological intervention R858-859.7 FOS: Physical sciences Medicine (miscellaneous) Health Informatics Physics and Society (physics.soc-ph) 02 engineering and technology Disease cluster Quantitative Biology - Quantitative Methods 03 medical and health sciences 0302 clinical medicine Country level Health Information Management Pandemic 0202 electrical engineering electronic engineering information engineering Global mobility 030212 general & internal medicine Quantitative Methods (q-bio.QM) Social and Information Networks (cs.SI) Public economics Computer Science - Social and Information Networks Computer Science Applications FOS: Biological sciences Business |
| Zdroj: | npj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021) |
| Popis: | The implementation of governmental Non-Pharmaceutical Interventions (NPIs) has been the primary means of controlling the spread of the COVID-19 disease. The intended effect of these NPIs has been to reduce mobility. A strong reduction in mobility is believed to have a positive effect on the reduction of COVID-19 transmission by limiting the opportunity for the virus to spread in the population. Due to the huge costs of implementing these NPIs, it is essential to have a good understanding of their efficacy. Using global mobility data, released by Apple and Google, and ACAPS NPI data, we investigate the proportional contribution of NPIs on i) size of the change (magnitude) of transition between pre- and post-lockdown mobility levels and ii) rate (gradient) of this transition. Using generalized linear models to find the best fit model we found similar results using Apple or Google data. NPIs found to impact the magnitude of the change in mobility were: Lockdown measures (Apple, Google Retail and Recreation (RAR) and Google Transit and Stations (TS)), declaring a state of emergency (Apple, Google RAR and Google TS), closure of businesses and public services (Google RAR) and school closures (Apple). Using cluster analysis and chi square tests we found that closure of businesses and public services, school closures and limiting public gatherings as well as border closures and international flight suspensions were closely related. The implementation of lockdown measures and limiting public gatherings had the greatest effect on the rate of mobility change. In conclusion, we were able to quantitatively assess the efficacy of NPIs in reducing mobility, which enables us to understand their fine grained effects in a timely manner and therefore facilitate well-informed and cost-effective interventions. 16 pages, 6 figures |
| Databáze: | OpenAIRE |
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