Literature DB >> 33597556

Use Internet search data to accurately track state level influenza epidemics.

Shihao Yang1, Shaoyang Ning2, S C Kou3.   

Abstract

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.

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Year:  2021        PMID: 33597556      PMCID: PMC7889878          DOI: 10.1038/s41598-021-83084-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  25 in total

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5.  Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.

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Authors:  Michael W Davidson; Dotan A Haim; Jennifer M Radin
Journal:  Sci Rep       Date:  2015-01-29       Impact factor: 4.379

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Authors:  Shaoyang Ning; Shihao Yang; S C Kou
Journal:  Sci Rep       Date:  2019-03-27       Impact factor: 4.379

8.  Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches.

Authors:  Fred S Lu; Mohammad W Hattab; Cesar Leonardo Clemente; Matthew Biggerstaff; Mauricio Santillana
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Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

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Authors:  Shihao Yang; Samuel C Kou; Fred Lu; John S Brownstein; Nicholas Brooke; Mauricio Santillana
Journal:  PLoS Comput Biol       Date:  2017-07-20       Impact factor: 4.475

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  3 in total

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