Bibliographic record
Abstract
Corona Virus Disease 2019 (COVID-19), short for “new coronavirus pneumonia” and named “2019 coronavirus disease” by the World Health organization, refers to pneumonia caused by the new 2019 coronavirus infection. COVID-19 is highly transmissible and is spreading rapidly around the world, posing a threat to human health and already having a huge impact on the world economy. Therefore, it is very important and necessary to predict the trend of the number of patients with COVID-19. The present work proposes learning models. There have been many previous studies using deep learning neural networks to predict time series, and the results have been shown to be outstanding. The present work will change the forecast format from using the entire segment of the data to forecast a day or segment to a roller forecast format. We will start from Convolutional Neural Network (CNN) and then use simple Recurrent Neural Networks (RNN) and The Gated Recurrent Units (GRUs) cells along with Long Short-Term Memory (LSTM) cells to predict the deaths and cases in US. We have used publicly available data from John Hopkins University’s COVID-19 database. In this study we will find the advantages and disadvantages of roller prediction and the limitations of different models and predict future trends, which will play a positive role in the research and prediction of the prevention and control of the epidemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".