Nonlinear controller design for a fractional extended model of COVID-19 outbreak using feedback linearization method
Bibliographic record
Abstract
This paper proposes a novel fractional-order epidemic model for the COVID-19 outbreak using the Caputo derivative that incorporates various intervention policies to manage the spread of the disease. A total of eight state variables were considered in this nonlinear model, namely, susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible. Two possible outbreak scenarios were considered to control the disease before and after vaccine discovery. The proposed system was designed using the feedback linearization method, allowing to develop a suitable controller for reducing susceptible, exposed, and infected populations. A comparative study with previous work was conducted based on Canada’s reported cases to demonstrate the advantages and disadvantages of the proposed COVID-19 outbreak control strategy using this model. The simulation results confirmed that the proposed fractional controller could effectively track the desired goals, including an exponential decrease in infected, exposed, and susceptible populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".