Control of malaria outbreak using a non‐linear robust strategy with adaptive gains
Bibliographic record
Abstract
The aim of this study is to develop a non‐linear robust controller with adaptive gains in order to prevent malaria epidemic as a positive system with an uncertain model. The malaria outbreak is modelled by seven non‐linear coupled differential equations for the population variables: susceptible, exposed, symptomatic infected and recovered humans and the susceptible, exposed and infected mosquitoes. The non‐linear robust adaptive integral‐sliding‐mode controller is developed in order to appropriately adjust the use of treated bednets, treatment rate of infected individuals and the use of insecticide spray to control malaria epidemic. Accordingly, the numbers of exposed and infected humans and infected mosquitoes are decreased to zero by employing the designed control scheme. However, the numbers of susceptible individuals and mosquitoes are increased due to their birth rates and loss of malaria immunity in recovered individuals. The Lyapunov stability theorem is used to prove the stability, robustness and tracking convergence of the closed‐loop system in the presence of modelling uncertainties. The simulation results demonstrate that by increasing the therapy time interval, the use of treated bednets and insecticide spray is decreased; however, a higher treatment rate is required for the infected population.
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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 | 0.001 |
| 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 | 0.001 |
| 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".