Modeling the impact of treatment, vaccination and sterile mosquito release on malaria transmission
Bibliographic record
Abstract
In this study, we develop and analyze a mathematical model to investigate the effects of treatment, vaccination, and sterile male mosquito release on malaria transmission. The model incorporates different levels of immunity, distinguishing between non-immune and semi-immune populations to better capture the dynamics of malaria spread and control. Using the next-generation matrix method, we compute the control reproduction number and establish the local asymptotic stability of the disease-free equilibrium when $\mathcal{R}_C<1$. A global sensitivity analysis with the reproduction number as the outcome variable is conducted to determine key parameters influencing malaria transmission. Additionally, we formulate and analyze an optimal control problem incorporating vaccination, treatment, and sterile male mosquito release as controls, and carry out a cost-effectiveness analysis to assess the economic feasibility of various intervention strategies. Our results suggest that a comprehensive intervention strategy that integrates treatment, vaccination, and sterile mosquito release is the most effective approach for reducing malaria transmission. However, from a cost-effectiveness perspective, prioritizing vaccination and treatment is the most feasible option in resource-limited settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".