ArticleIEEE transactions on big data2021
An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak.
Article in IEEE transactions on big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Predictive and interpretable machine learning for COVID-19 resurgences: the role of SARS-CoV-2 variants in the post-pandemic era.BMC infectious diseases · 2025Article
- AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers.PLOS digital health · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.PLoS computational biology · 2025Article
- MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting.Entropy (Basel, Switzerland) · 2024Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- Article
- DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.The protein journal · 2023Article
- A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction.Heliyon · 2023Article
- Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data.Biomimetics (Basel, Switzerland) · 2023Article
- Innovative applications of artificial intelligence in zoonotic disease management.Science in One Health · 2023Review
- PAN-cODE: COVID-19 forecasting using conditional latent ODEs.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- COVID-19 forecasting using new viral variants and vaccination effectiveness models.Computers in biology and medicine · 2022Article
- An AI-based disease detection and prevention scheme for COVID-19.Computers & electrical engineering : an international journal · 2022Article
- A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.International journal of environmental research and public health · 2022Article
- Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study.International journal of environmental research and public health · 2022Article
- Adaptively temporal graph convolution model for epidemic prediction of multiple age groups.Fundamental research · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.
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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.