ArticleEBioMedicine2023
Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Leveraging synthetic and genetic data to improve epidemic forecasting.PLoS computational biology · 2026Article
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026Article
- A dual-branch deep learning framework for tiered early warning of COVID-19 utilizing wastewater data.Journal of water and health · 2026Article
- Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.PLoS computational biology · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Association between vaccination rates and COVID-19 health outcomes in the United States: a population-level statistical analysis.BMC public health · 2024Article
- CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting.Virus evolution · 2024Article
- Development of an Early-Phase Local Model for Pandemics Using Public Health Data: Application to the COVID-19 Pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination.Diseases (Basel, Switzerland) · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic.Scientific data · 2023Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSince the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term.
methodHere we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases.
findingsThe proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants.
interpretationBased on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk.
fundingThis work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
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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.