ArticleJournal of applied statistics2025
COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States.
Article in Journal of applied statistics, 2025. 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.
- Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data.Biomimetics (Basel, Switzerland) · 2023Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- A coarse-refine segmentation network for COVID-19 CT images.IET image processing · 2022Article
- Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.Knowledge-based systems · 2021Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques.Infectious Disease Modelling · 2021Article
- DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions.IEEE access : practical innovations, open solutions · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.
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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 Socratic graph.