Parasite Control Strategies: Ecological Interventions
Bibliographic record
Abstract
Parasites have to overcome a series of barriers before infecting humans or animals. Interventions that focus on one or more of these impediments may help to prevent parasitic infections. Here, we explain how ecological interventions can be used to complement traditional control methods such as vaccines, drug treatments and disinfection. To overcome a few major limitations of traditional control methods such as drug resistance, environmental pollution and the adverse effects on non‐target organisms, ecological interventions are now at the vanguard of parasite control. Globally, promising results have been achieved by the application of ecological interventions such as mitigation and control strategies. This chapter encompasses a review of the history, limitations and success stories of the different types of strategies used to manipulate the ecology for the control of parasites. These strategies include the classification of parasites (based on their mode of spread) and designing control programmes such as habitat control, intermediate host control and vector control. Moreover, we discuss case studies of the successful implementation of ecological interventions, the challenges encountered during their implementation and future perspectives that will guide policies to improve parasite control through ecological measures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".