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Internet search data could Be used as novel indicator for assessing COVID-19 epidemic
The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585146/ https://www.ncbi.nlm.nih.gov/pubmed/33134612 http://dx.doi.org/10.1016/j.idm.2020.10.001 |
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author | Li, Kang Liang, Yanling Li, Jianjun Liu, Meiliang Feng, Yi Shao, Yiming |
author_facet | Li, Kang Liang, Yanling Li, Jianjun Liu, Meiliang Feng, Yi Shao, Yiming |
author_sort | Li, Kang |
collection | PubMed |
description | The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early indicator for emerging epidemic. After the keyword filtering and Index composition, we found that there were close correlations between Composite Index and suspected cases for COVID-19 (r = 0.921, P < 0.05). The Search Index was applied for the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to quantify the relationship. Compared with the model based on surveillance data only, the ARIMAX model had smaller Akaike Information Criterion (AIC = 403.51) and the most accurate predictive values. Overall, the Internet search data could serve as a convenient indicator for predicting the epidemic and to monitor its trends. |
format | Online Article Text |
id | pubmed-7585146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | KeAi Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-75851462020-10-30 Internet search data could Be used as novel indicator for assessing COVID-19 epidemic Li, Kang Liang, Yanling Li, Jianjun Liu, Meiliang Feng, Yi Shao, Yiming Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early indicator for emerging epidemic. After the keyword filtering and Index composition, we found that there were close correlations between Composite Index and suspected cases for COVID-19 (r = 0.921, P < 0.05). The Search Index was applied for the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to quantify the relationship. Compared with the model based on surveillance data only, the ARIMAX model had smaller Akaike Information Criterion (AIC = 403.51) and the most accurate predictive values. Overall, the Internet search data could serve as a convenient indicator for predicting the epidemic and to monitor its trends. KeAi Publishing 2020-10-03 /pmc/articles/PMC7585146/ /pubmed/33134612 http://dx.doi.org/10.1016/j.idm.2020.10.001 Text en © 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu Li, Kang Liang, Yanling Li, Jianjun Liu, Meiliang Feng, Yi Shao, Yiming Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title | Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title_full | Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title_fullStr | Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title_full_unstemmed | Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title_short | Internet search data could Be used as novel indicator for assessing COVID-19 epidemic |
title_sort | internet search data could be used as novel indicator for assessing covid-19 epidemic |
topic | Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585146/ https://www.ncbi.nlm.nih.gov/pubmed/33134612 http://dx.doi.org/10.1016/j.idm.2020.10.001 |
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