Public mobility data enables COVID-19 forecasting and management at local and global scales

Abstract Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility—collected by Google, Facebook, and other providers—can be used...

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Autores principales:	Cornelia Ilin, Sébastien Annan-Phan, Xiao Hui Tai, Shikhar Mehra, Solomon Hsiang, Joshua E. Blumenstock
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/235c1d4408f34a639c279cad798edae3
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spelling	oai:doaj.org-article:235c1d4408f34a639c279cad798edae32021-12-02T16:10:38ZPublic mobility data enables COVID-19 forecasting and management at local and global scales10.1038/s41598-021-92892-82045-2322https://doaj.org/article/235c1d4408f34a639c279cad798edae32021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92892-8https://doaj.org/toc/2045-2322Abstract Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility—collected by Google, Facebook, and other providers—can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases. An advantage of the approach is that it involves minimal assumptions about disease dynamics, and requires only publicly-available data. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. We find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections.Cornelia IlinSébastien Annan-PhanXiao Hui TaiShikhar MehraSolomon HsiangJoshua E. BlumenstockNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cornelia Ilin Sébastien Annan-Phan Xiao Hui Tai Shikhar Mehra Solomon Hsiang Joshua E. Blumenstock Public mobility data enables COVID-19 forecasting and management at local and global scales
description	Abstract Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility—collected by Google, Facebook, and other providers—can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases. An advantage of the approach is that it involves minimal assumptions about disease dynamics, and requires only publicly-available data. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. We find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections.
format	article
author	Cornelia Ilin Sébastien Annan-Phan Xiao Hui Tai Shikhar Mehra Solomon Hsiang Joshua E. Blumenstock
author_facet	Cornelia Ilin Sébastien Annan-Phan Xiao Hui Tai Shikhar Mehra Solomon Hsiang Joshua E. Blumenstock
author_sort	Cornelia Ilin
title	Public mobility data enables COVID-19 forecasting and management at local and global scales
title_short	Public mobility data enables COVID-19 forecasting and management at local and global scales
title_full	Public mobility data enables COVID-19 forecasting and management at local and global scales
title_fullStr	Public mobility data enables COVID-19 forecasting and management at local and global scales
title_full_unstemmed	Public mobility data enables COVID-19 forecasting and management at local and global scales
title_sort	public mobility data enables covid-19 forecasting and management at local and global scales
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/235c1d4408f34a639c279cad798edae3
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Public mobility data enables COVID-19 forecasting and management at local and global scales

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