Evidence map›Paper›PMID 40269734›Full record

ArticleBMC medical research methodology2025

Enhancing public health surveillance: SARIMAX model incorporating Baidu search index for HCV prediction in China.

Huan Ning, Yingji Lan, Qianqian Huang, Tingyan Luo, Jing Yang, Jie Zhou, Zhuoxin Li, Ping Cui, Hao Liang, Jiegang Huang

Abstract read
In one paragraph

Article in BMC medical research methodology, 2025. 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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Huan Ning *School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Yingji Lan *School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Qianqian Huang *School of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Tingyan LuoSchool of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Jing YangSchool of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Jie ZhouSchool of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Zhuoxin LiSchool of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Ping CuiLife Science Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China.
Hao LiangGuangxi Key Laboratory of AIDS Prevention and Treatment, Nanning, 530021, Guangxi, China.
Jiegang HuangSchool of Public Health, Guangxi Medical University, Nanning, 530021, Guangxi, China. jieganghuang@gxmu.edu.cn.

Funding

Natural Science Foundation of Guangxi Zhuang Autonomous Region 2023GXNSFDA026036
6 · The paper itself

Abstract

backgroundHepatitis C virus (HCV) has become a serious global public health issue. Existing study has confirmed the potential role of internet search data in the prediction of infectious diseases. This study aims to explore predictive models suitable for HCV, providing references and recommendations for actively responding to the World Health Organization's strategy to eliminate HCV by 2030.

methodsWe collected the publicly available daily reported case numbers of HCV and the daily search volume of HCV-related keywords in Baidu Search Index (BSI) from January 2011 to September 2023. Identify keywords highly correlated with HCV using Spearman rank correlation and time series cross-correlation analysis to construct a comprehensive search index. Finally, seasonal autoregressive integrated moving average (SARIMA) and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were developed based on monthly HCV case numbers and the comprehensive search index (CSI). The performance of these predictive models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

resultsFrom January 2011 to September 2023, China reported a cumulative total of 2,949,160 HCV cases, with a monthly average case number of 19,276. The incidence of HCV is highest in March each year, with a slight decrease in February, exhibiting a seasonal cycle pattern. Influenced by the COVID-19 pandemic, the incidence of HCV showed a notable decrease in 2020 and 2022, but overall, it has been on an upward trend. After correlation analysis, a total of five keywords were included, with a potential time lag of 0 months for the keywords. The selected SARIMA(0,1,1)(2,1,1)

conclusionsThe findings suggest that the SARIMA(0,1,1)(2,1,1)

Indexed as

Hepatitis CPublic Health SurveillanceSearch EngineChinaCOVID-19HepacivirusHumansIncidenceInternetModels, StatisticalPublic HealthSeasonsBaidu search indexHCV eliminationHepatitis C virusSARIMAX modelTime series

Identifiers

PMID40269734
PMCPMC12016294

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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.