Evidence map›Paper›PMID 38966307›Full record

ArticleChina CDC weekly2024

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

Xuan Han, Jiao Yang, Yan Luo, Dazhu Huo, Xuya Yu, Xuancheng Hu, Ling Xin, Liuyang Yang, Hualei Xin, Ting Zhang and 2 more

Abstract read
In one paragraph

Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

12 authors.

Xuan HanSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Jiao YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Yan LuoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Dazhu HuoSchool of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xuya YuSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Xuancheng HuDepartment of management science and information system, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming City, Yunnan Province, China.
Ling XinSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Liuyang YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Hualei XinSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Ting ZhangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Zhongjie LiSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
Weizhong YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Indexed as

Baidu indexInfectious diseaseInfluenza-like illnessPopulation medicinePublic health

Identifiers

PMID38966307
PMCPMC11219297

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Registered trials

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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.