ArticleScientific reports2021
Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 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
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Influenza forecasting method based on dual-chan nel feature fusion of VMD decomposition.Scientific reports · 2026Article
- Leveraging universal and transfer learning models for influenza prediction in Thailand.Scientific reports · 2026Article
- Spatiotemporal modeling of regional short-term pertussis transmission risk using a propagation-enhanced prediction framework.Frontiers in public health · 2026Article
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Traffic-driven spatio-temporal prediction for fine-scale epidemic outbreaks in China.BMC infectious diseases · 2025Article
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.Scientific reports · 2025Article
- MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data.Scientific reports · 2024Article
- Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.JAMIA open · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- Article
- Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.Sustainable cities and society · 2023Article
- Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.Nature communications · 2023Article
- Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks.Scientific reports · 2023Article
- Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.EBioMedicine · 2023Article
- Forecasting COVID-19 and Other Infectious Diseases for Proactive Policy: Artificial Intelligence Can Help.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Sensor Data Prediction in Missile Flight Tests.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.
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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.