Simulating and Predicting the Active Cases and Hospitalization Considering the Second Wave of COVID-19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has been an ongoing threat to the world's health system. Millions of people died all over the world because of this deadly virus outbreak. Although health sectors are equipped with modern technologies yet struggling every day to control this outbreak. However, predicting the active COVID-19 cases and hospitalization in advance can be helpful to minimize the catastrophe of this persistent outbreak. This study proposed a novel Agent-based modelling (ABM) framework based on various temporal and non-pharmaceuticals parameters to predict active cases and hospitalization cases. We evaluated the model's performance based on COVID-19 data of Windsor-Essex county region of Ontario, Canada, and eventually achieved satisfactory results in predicting active cases and hospitalization. Experimental results have demonstrated that the simulations provide helpful information that could help take advanced steps to cover up for the shortage in hospital resources and take necessary steps to reduce the number of infections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".