Forecasting influenza activity using machine-learned mobility map

Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple sp...

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Main Authors:	Srinivasan Venkatramanan, Adam Sadilek, Arindam Fadikar, Christopher L. Barrett, Matthew Biggerstaff, Jiangzhuo Chen, Xerxes Dotiwalla, Paul Eastham, Bryant Gipson, Dave Higdon, Onur Kucuktunc, Allison Lieber, Bryan L. Lewis, Zane Reynolds, Anil K. Vullikanti, Lijing Wang, Madhav Marathe
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-02-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-21018-5

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spelling	doaj-c02c4522aef64826b5c7b971f6fda3702021-02-14T12:11:50ZengNature Publishing GroupNature Communications2041-17232021-02-0112111210.1038/s41467-021-21018-5Forecasting influenza activity using machine-learned mobility mapSrinivasan Venkatramanan0Adam Sadilek1Arindam Fadikar2Christopher L. Barrett3Matthew Biggerstaff4Jiangzhuo Chen5Xerxes Dotiwalla6Paul Eastham7Bryant Gipson8Dave Higdon9Onur Kucuktunc10Allison Lieber11Bryan L. Lewis12Zane Reynolds13Anil K. Vullikanti14Lijing Wang15Madhav Marathe16Biocomplexity Institute and Initiative, University of VirginiaGoogle Inc.Argonne National LaboratoryBiocomplexity Institute and Initiative, University of VirginiaInfluenza Division, Centers for Disease Control and PreventionBiocomplexity Institute and Initiative, University of VirginiaGoogle Inc.Google Inc.Google Inc.Department of Statistics, Virginia TechGoogle Inc.Google Inc.Biocomplexity Institute and Initiative, University of VirginiaTorc RoboticsBiocomplexity Institute and Initiative, University of VirginiaBiocomplexity Institute and Initiative, University of VirginiaBiocomplexity Institute and Initiative, University of VirginiaHuman mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.https://doi.org/10.1038/s41467-021-21018-5
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Srinivasan Venkatramanan Adam Sadilek Arindam Fadikar Christopher L. Barrett Matthew Biggerstaff Jiangzhuo Chen Xerxes Dotiwalla Paul Eastham Bryant Gipson Dave Higdon Onur Kucuktunc Allison Lieber Bryan L. Lewis Zane Reynolds Anil K. Vullikanti Lijing Wang Madhav Marathe
spellingShingle	Srinivasan Venkatramanan Adam Sadilek Arindam Fadikar Christopher L. Barrett Matthew Biggerstaff Jiangzhuo Chen Xerxes Dotiwalla Paul Eastham Bryant Gipson Dave Higdon Onur Kucuktunc Allison Lieber Bryan L. Lewis Zane Reynolds Anil K. Vullikanti Lijing Wang Madhav Marathe Forecasting influenza activity using machine-learned mobility map Nature Communications
author_facet	Srinivasan Venkatramanan Adam Sadilek Arindam Fadikar Christopher L. Barrett Matthew Biggerstaff Jiangzhuo Chen Xerxes Dotiwalla Paul Eastham Bryant Gipson Dave Higdon Onur Kucuktunc Allison Lieber Bryan L. Lewis Zane Reynolds Anil K. Vullikanti Lijing Wang Madhav Marathe
author_sort	Srinivasan Venkatramanan
title	Forecasting influenza activity using machine-learned mobility map
title_short	Forecasting influenza activity using machine-learned mobility map
title_full	Forecasting influenza activity using machine-learned mobility map
title_fullStr	Forecasting influenza activity using machine-learned mobility map
title_full_unstemmed	Forecasting influenza activity using machine-learned mobility map
title_sort	forecasting influenza activity using machine-learned mobility map
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-02-01
description	Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.
url	https://doi.org/10.1038/s41467-021-21018-5
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Forecasting influenza activity using machine-learned mobility map

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