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Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss

AIM: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. METHODS: Data on confirmed COVID-19 cases from 6 January 2020 to 26 Dece...

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Autores principales: Chen, Jingguo, Mi, Hao, Fu, Jinyu, Zheng, Haitian, Zhao, Hongyue, Yuan, Rui, Guo, Hanwei, Zhu, Kang, Zhang, Ya, Lyu, Hui, Zhang, Yitong, She, Ningning, Ren, Xiaoyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751448/
https://www.ncbi.nlm.nih.gov/pubmed/36530657
http://dx.doi.org/10.3389/fpubh.2022.1025658
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author Chen, Jingguo
Mi, Hao
Fu, Jinyu
Zheng, Haitian
Zhao, Hongyue
Yuan, Rui
Guo, Hanwei
Zhu, Kang
Zhang, Ya
Lyu, Hui
Zhang, Yitong
She, Ningning
Ren, Xiaoyong
author_facet Chen, Jingguo
Mi, Hao
Fu, Jinyu
Zheng, Haitian
Zhao, Hongyue
Yuan, Rui
Guo, Hanwei
Zhu, Kang
Zhang, Ya
Lyu, Hui
Zhang, Yitong
She, Ningning
Ren, Xiaoyong
author_sort Chen, Jingguo
collection PubMed
description AIM: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. METHODS: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords “loss of smell” and “loss of taste” were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. RESULTS: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. CONCLUSION: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1–3 weeks in advance.
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spelling pubmed-97514482022-12-16 Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss Chen, Jingguo Mi, Hao Fu, Jinyu Zheng, Haitian Zhao, Hongyue Yuan, Rui Guo, Hanwei Zhu, Kang Zhang, Ya Lyu, Hui Zhang, Yitong She, Ningning Ren, Xiaoyong Front Public Health Public Health AIM: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. METHODS: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords “loss of smell” and “loss of taste” were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. RESULTS: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. CONCLUSION: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1–3 weeks in advance. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9751448/ /pubmed/36530657 http://dx.doi.org/10.3389/fpubh.2022.1025658 Text en Copyright © 2022 Chen, Mi, Fu, Zheng, Zhao, Yuan, Guo, Zhu, Zhang, Lyu, Zhang, She and Ren. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Chen, Jingguo
Mi, Hao
Fu, Jinyu
Zheng, Haitian
Zhao, Hongyue
Yuan, Rui
Guo, Hanwei
Zhu, Kang
Zhang, Ya
Lyu, Hui
Zhang, Yitong
She, Ningning
Ren, Xiaoyong
Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title_full Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title_fullStr Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title_full_unstemmed Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title_short Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss
title_sort construction and validation of a covid-19 pandemic trend forecast model based on google trends data for smell and taste loss
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751448/
https://www.ncbi.nlm.nih.gov/pubmed/36530657
http://dx.doi.org/10.3389/fpubh.2022.1025658
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