ArticleRoyal Society open science2020
Early warning of some notifiable infectious diseases in China by the artificial neural network.
Article in Royal Society open science, 2020. 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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review.Frontiers in public health · 2025Pooled it
- A framework using large time series model for early warning of infectious diseases.Infectious Disease Modelling · 2026Article
- Machine learning analysis of coagulation-related genes for breast cancer diagnosis and prognosis prediction.Scientific reports · 2025Article
- Deep learning for disease outbreak prediction: a parallel LSTM-CNN model.Journal of the Royal Society, Interface · 2025Article
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.BMC infectious diseases · 2024Article
- Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.Frontiers in public health · 2022Article
- Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics.Frontiers in immunology · 2022Article
- Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning.Frontiers in psychology · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.
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