Prediction of COVID-19 Cases based on Human Behavior using DNN Regressor for Canada
Bibliographic record
Abstract
The proposed work utilizes Deep Neural Network (DNN) regression model to predict the total number of cases, new cases, and death cases in Canada. It is evaluated based on human behavior such as isolation, wearing mask outside home, contact with symptomatic person, washing hands, and other behaviors. The dataset is collected for the period of March 9th, 2020 to November 2nd, 2020 for Canada. The proposed methodology uses multiple-input deep neural network regression model with Rectified Linear Unit (Re LU) Function as the activation function, five non-linear dense layers of 64 Unit and a single unit of last layer as output for the curve fitting. The dataset is split into train and test sets with test size 20% and training size 80%. A nonlinear regression model is applied to the normalized data for making accurate predictions. The model performance is evaluated based on Root Mean Square Error (RMSE). Also, the Mean Absolute Error (MAE) is estimated for the model to quantify the error between predicted and true values. The results show that the proposed machine learning (ML) method predicts with high accuracy and can also be a convenient tool in making predictions for other countries.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".