Evidence map›Paper›PMID 36530657›Full record

ArticleFrontiers in public health2022

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

Jingguo Chen, Hao Mi, Jinyu Fu, Haitian Zheng, Hongyue Zhao, Rui Yuan, Hanwei Guo, Kang Zhu, Ya Zhang, Hui Lyu and 3 more

Abstract read
PubMed Full text at PMC Publisher
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Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jingguo ChenDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Hao MiSchool of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Jinyu FuSchool of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Haitian ZhengSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
Hongyue ZhaoHealth Science Center, Xi'an Jiaotong University, Xi'an, China.
Rui YuanHealth Science Center, Xi'an Jiaotong University, Xi'an, China.
Hanwei GuoSchool of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Kang ZhuDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ya ZhangDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Hui LyuDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yitong ZhangDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ningning SheDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Xiaoyong RenDepartment of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 ( 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.

Indexed as

AgeusiaCOVID-19Olfaction DisordersHumansPandemicsSARS-CoV-2Search EngineSmellUnited Statesbig dataCOVID-19predictionsmelltaste

Identifiers

PMID36530657
PMCPMC9751448

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.