An Adaptive Compensation Strategy for Optimizing Release of Sterile Mosquitoes to Achieve Better Wild Mosquito Population Suppression: Insights from a Delay Differential Model and Analysis
Bibliographic record
Abstract
Abstract. The sterile insect release technique (SIRT) has emerged as an effective mosquito control technique, and the Wolbachia-infection technology (WIT) for suppressing wild mosquito populations with repeated releases of infected mosquitoes can result in infertility analogous to that of SIRT by inducing complete cytoplasmic incompatibility. The successful implementation of WIT in multiple regions and climate zones has shown potential to significantly reduce the risk of transmitting mosquito-borne diseases. To expand this success globally, it is crucial to optimize release strategies, particularly by minimizing the number and frequency of released mosquitoes. In this study, we propose an adaptive compensation strategy and develop a highly nonlinear delay differential equation to analyze its implementation and efficacy, taking into consideration the adult mosquito mating probability. The nonlinearity involves an adaptive release of sterile mosquitoes, based on the surveillance of adult mosquitoes in the field. We show that the system may exhibit complex dynamical behavior including global stability, semistability, multistability of equilibria, and Hopf bifurcation of periodic solutions. Our simulation reveals that our proposed adaptive compensation strategy outperforms the current release strategy implemented by Guangzhou mosquito control program, which employs a conventional fixed 5:1 releasing ratio between the infected and wild males, in terms of average suppression rate, released amount and releasing frequency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".