ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Integrating Multiple Data Sources and Learning Models to Predict Infectious Diseases in China.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
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- Assessing the potential for application of machine learning in predicting weather-sensitive waterborne diseases in selected districts of Tanzania.Frontiers in artificial intelligence · 2025Article
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- Predicting diarrhoea outbreaks with climate change.PloS one · 2022Article
- The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review.Heliyon · 2021Review
- ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries.Journal of healthcare informatics research · 2021Article
- Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering.JMIR public health and surveillance · 2020Article
- Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreaks of infectious diseases do not only endanger people's lives and property, but can also result in negative social impact and economic loss. Therefore, establishing early warning technologies for infectious diseases is of great value. This paper was built on the historical morbidity and mortality incidence data of infectious diseases, including typhoid fever, Hemorrhagic Fever with Renal Syndrome (HFRS), mumps, scarlatina, malaria, dysentery, pertussis, conjunctivitis, pulmonary tuberculosis, diarrhea from 2012 to 2016 in China. We also integrated search engine query data and seasonal information into the prediction models. Multiple models for prediction, including linear model, time series analysis model, boosting tree model and deep learning model (recurrent neural network, RNN) were constructed in order to predict the morbidity incidence of 10 infectious diseases. The RNN model has better predictive capability for these diseases. The improvement of techniques for infectious disease prediction can facilitate constructive and positive change towards disease prevention.
Identifiers
31259024PMC6568090W2991255706What 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.