Geographically regulated designs of incidence surveys can match the precision of classical survey designs whilst requiring smaller sample sizes: the case of snakebite envenoming in Sri Lanka.
Bibliographic record
Abstract
BACKGROUND: Snakebite envenoming is a neglected tropical disease. Data from the worst affected countries are limited because conducting epidemiological surveys is challenging. We assessed the utility of inhibitory geostatistical design with close pairs (ICP) to estimate snakebite envenoming incidence. METHODS: The National Snakebite Survey (NSS) in Sri Lanka adopted a multistage cluster sampling design, based on population distribution, targeting 1% of the country's population. Using a simulation-based study, we assessed predictive efficiency of ICP against a classical survey design at different fractions of the original sample size of the NSS. We also assessed travel distance, time taken to complete the survey, and sensitivity and specificity for detecting high-risk areas for snake envenoming, when using these methods. RESULTS: A classical survey design with 33% of the original NSS sample size was able to yield a similar predictive efficiency. ICP yielded the same at 25% of the NSS sample size, a 25% reduction in sample size compared with a classical survey design. ICP showed >80% sensitivity and specificity for detecting high-risk areas of envenoming when the sampling fraction was >20%. When ICP was adopted with 25% of the original NSS sample size, travel distance was reduced by >40% and time to conduct the survey was reduced by >75%. CONCLUSIONS: This study showed that snakebite envenoming incidence can be estimated by adopting an ICP design with similar precision at a lower sample size than a classical design. This would substantially save resources and time taken to conduct epidemiological surveys and may be suited for low-resource settings.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".