ArticleNature communications2021
Deep learning of contagion dynamics on complex networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Article
- Survey on mathematical modeling of infectious disease dynamics: insights and applications.BMC infectious diseases · 2026Review
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Learning interpretable network dynamics via universal neural symbolic regression.Nature communications · 2025Article
- A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks.Scientific reports · 2025Article
- A quantum mechanics-based framework for infectious disease modeling.Scientific reports · 2025Article
- Identifying Influential Nodes Based on Evidence Theory in Complex Network.Entropy (Basel, Switzerland) · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Complexity of social media in the era of generative AI.National science review · 2025Article
- Deep learning resilience inference for complex networked systems.Nature communications · 2024Article
- Article
- Reconstructing networks from simple and complex contagions.Physical review. E · 2024Article
- Duality between predictability and reconstructability in complex systems.Nature communications · 2024Article
- Nonlinear bias toward complex contagion in uncertain transmission settings.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.Entropy (Basel, Switzerland) · 2023Article
- Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.Entropy (Basel, Switzerland) · 2023Article
- Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units.Scientific reports · 2023Article
- Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.
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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.