ArticleScientific reports2021
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- The red cell distribution width to albumin ratio as a novel predictor of 180-day mortality in lung cancer patients.Scientific reports · 2026Article
- Article
- A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data.BMC medical research methodology · 2025Article
- A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data.medRxiv : the preprint server for health sciences · 2025Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.Scientific reports · 2024Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study.Research square · 2023Article
- COVID-19 forecasts using Internet search information in the United States.Scientific reports · 2022Article
- Interpretable Temporal Attention Network for COVID-19 forecasting.Applied soft computing · 2022Article
- Iterative data-driven forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level.Scientific reports · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.
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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.