ArticleNonlinear dynamics2022
Estimating the state of epidemics spreading with graph neural networks.
Article in Nonlinear dynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- Inferring pathogen superspreading potential using early spatial spread patterns.medRxiv : the preprint server for health sciences · 2025Article
- AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers.PLOS digital health · 2025Article
- Test allocation based on risk of infection from first and second order contact tracing.PloS one · 2025Article
- MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting.Entropy (Basel, Switzerland) · 2024Article
- Article
- A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.Entropy (Basel, Switzerland) · 2023Article
- Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- A Bayesian generative neural network framework for epidemic inference problems.Scientific reports · 2022Article
- Preface to the special issue "Complex dynamics of COVID-19: modeling, prediction and control (part II)".Nonlinear dynamics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.
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.