ArticlePloS one2021
The predictive skill of convolutional neural networks models for disease forecasting.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence Applications in MRI for the Diagnosis and Management of Osteonecrosis of the Femoral Head: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Influenza forecasting method based on dual-chan nel feature fusion of VMD decomposition.Scientific reports · 2026Article
- Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift.Frontiers in artificial intelligence · 2026Article
- Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Enhancing epidemic forecasting with a physics-informed spatial identity neural network.PloS one · 2025Article
- Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman.Bioengineering (Basel, Switzerland) · 2023Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block-temporal convolutional networks and simple neural attentive meta-learners-for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.
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