ArticleInternational journal of environmental research and public health2018
Predicting Infectious Disease Using Deep Learning and Big Data.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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Who cites it
91 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- Advancing outbreak detection: Hybridizing machine learning with wavelets for weekly dengue case forecasting.PLoS neglected tropical diseases · 2026Article
- Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.Tropical medicine & international health : TM & IH · 2026Article
- Machine Learning-Based Severity Stratification for Smart Preventive Decision Support: Evidence from Measles Surveillance in a Resource-Constrained Region.Journal of clinical medicine · 2026Article
- Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction.Journal of public health research · 2026Review
- Role of Digital Health Technologies and Artificial Intelligence in Modern Public Health Surveillance.Cureus · 2026Review
- A data privacy protection method for infectious disease prediction models with balanced training speed and accuracy.Scientific reports · 2026Article
- Dynamic evaluation system for improving hospital outsourcing service performance: a G1-Critic and LSTM+Dropout approach.BMC health services research · 2026Article
- Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift.Frontiers in artificial intelligence · 2026Article
- Insights from Atangana-Baleanu fractional derivatives modeling of influenza epidemics and sensitivity analysis.Scientific reports · 2025Article
- Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990-2021.BMC infectious diseases · 2025Article
- Insight into public sentiment and demand in China's public health emergency response: a weibo data analysis.BMC public health · 2025Article
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm.Scientific reports · 2025Article
- Harnessing artificial intelligence for enhanced public health surveillance: a narrative review.Frontiers in public health · 2025Review
- Article
- Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review.PeerJ. Computer science · 2025Article
- Impact of Educational Intervention on Hygiene Knowledge and Practices of Sanitation Workers Globally: A Systematic Review.Scientifica · 2025Review
- Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks.PNAS nexus · 2024Article
- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
31 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.
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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.