ArticleBMJ open2017
Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- An ARIMAX model integrating Baidu index for improved hand, foot, and mouth disease incidence prediction.Scientific reports · 2026Article
- Barriers to translating continuous monitoring technologies for preventative medicine.Nature biomedical engineering · 2025Review
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Article
- Epidemiological characteristics and prediction model construction of hand, foot and mouth disease in Quzhou City, China, 2005-2023.Frontiers in public health · 2024Article
- Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.Environmental science and pollution research international · 2023Article
- Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.Environmental health and preventive medicine · 2023Article
- Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia.International journal of environmental research and public health · 2022Article
- Impact of a health campaign on Chinese public awareness of stroke: evidence from internet search data.BMJ open · 2021Article
- Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker.BMC infectious diseases · 2021Article
- Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost.PloS one · 2021Article
- COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis.The Science of the total environment · 2020Article
- Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?International journal of environmental research and public health · 2020Article
- Internet search data could Be used as novel indicator for assessing COVID-19 epidemic.Infectious Disease Modelling · 2020Article
- Weather effects on hand, foot, and mouth disease at individual level: a case-crossover study.BMC infectious diseases · 2019Article
- A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China.Scientific reports · 2019Article
- Spatial-temporal heterogeneity of hand, foot and mouth disease and impact of meteorological factors in arid/ semi-arid regions: a case study in Ningxia, China.BMC public health · 2019Article
- Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.BMC infectious diseases · 2019Article
- Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD.
designEcological study. SETTING AND
participantsInformation on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011-2014. Analyses were conducted at aggregate level and no confidential information was involved. OUTCOME MEASURES: A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI.
resultsA high correlation between HFMD incidence and BDI (
conclusionsAn ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings.
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.