ArticlePloS one2019
A dengue fever predicting model based on Baidu search index data and climate data in South China.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 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
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Decoding Dengue: A Global Perspective, History, Role, and Challenges.Pathogens (Basel, Switzerland) · 2025Review
- Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.Infectious diseases of poverty · 2023Observational
- Did the COVID-19 pandemic impact urticaria information-seeking behavior in China? A retrospective longitudinal study.Frontiers in public health · 2023Article
- The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study.JMIR public health and surveillance · 2022Observational
- Increasing public concern on insomnia during the COVID-19 outbreak in China: An info-demiology study.Heliyon · 2022Article
- Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.International journal of environmental research and public health · 2022Article
- An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China.PLoS computational biology · 2022Article
- Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.Frontiers in public health · 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
- Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review.Risk management and healthcare policy · 2022Review
- A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries.Life (Basel, Switzerland) · 2021Article
- Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques.Scientific reports · 2021Article
- Dynamics of dengue outbreaks in gangetic West Bengal: A trend and time series analysis.Journal of family medicine and primary care · 2020Article
- Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011-2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.
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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.