ArticleScientific reports2016
Using Baidu Search Index to Predict Dengue Outbreak in China.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic.Journal of epidemiology and global health · 2024Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Early prediction of the outbreak risk of dengue fever in Ba Ria-Vung Tau province, Vietnam: An analysis based on Google trends and statistical models.Infectious Disease Modelling · 2025Article
- Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.Journal of medical Internet research · 2023Article
- Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study.Journal of medical Internet research · 2023Article
- Public Concern about Haze and Ozone in the Era of Their Coordinated Control in China.International journal of environmental research and public health · 2023Article
- Influenza surveillance with Baidu index and attention-based long short-term memory model.PloS one · 2023Article
- Impact of the COVID-19 Pandemic on Meal Gathering in China.International journal of environmental research and public health · 2022Article
- Increasing public concern on insomnia during the COVID-19 outbreak in China: An info-demiology study.Heliyon · 2022Article
- Future perspectives of emerging infectious diseases control: A One Health approach.One health (Amsterdam, Netherlands) · 2022Article
- A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China.Frontiers in public health · 2022Article
- Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables.Healthcare (Basel, Switzerland) · 2021Article
- What motivates non-democratic leadership: Evidence from COVID-19 reopenings in China.Journal of public economics · 2021Article
- Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study.Journal of medical Internet research · 2020Article
- Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering.JMIR public health and surveillance · 2020Article
- Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics.International journal of environmental research and public health · 2020Article
- Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020Article
- Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China.Scientific reports · 2020Article
- Article
- Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic.Infectious diseases of poverty · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.