DSA-BEATS: Dual Self-Attention N-BEATS Model for Forecasting COVID-19 Hospitalization
Bibliographic record
Abstract
The high number of hospitalization cases of COVID-19 made public health providers overloaded. Forecasting the number of hospitalized patients related to COVID-19 can help public health providers to make informed decisions for controlling the spread. In this study, we present the Dual Self-Attention NBEATS (DSA-BEATS) model, a novel approach that effectively combines the self-attention mechanism of transformers with the proficiency of the N-BEATS model in dealing with multivariate forecasting problems. We expanded the dataset to a multivariate one by including data from Canadian transportation hub cities and SARS-CoV-2 RNA load in wastewater, which allowed for a more comprehensive modeling of the complex relationships impacting COVID-19 hospitalizations. These transportation hub cities were the major ports of entry for international travelers coming to the country. The DSA-BEATS model was tested on a 55-day test set with a 12-day horizon, resulting in a Mean Absolute Percentage Error (MAPE) of 14.23%, which implies an accuracy of 85.77%. These results demonstrate substantial improvements over state-of-the-art models such as N-BEATS and Informer, validating the efficacy of the DSA-BEATS model in accurately predicting COVID-19 hospitalizations. The study provides a significant contribution to the ongoing development of enhanced timeseries forecasting methods, particularly in the context of public health crises. The DSA-BEATS model’s ability to capture complex temporal relationships and effectively handle multivariate data inputs underscores its potential in a wide range of forecasting tasks beyond the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".