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 9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , 2020 to November 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> , 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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".