Evaluating Uncertainty in Dengue Seroprevalence in the Absence of a Gold Standard Diagnostic Test
Bibliographic record
Abstract
Background: Shortly after the introduction of the first licensed vaccine against dengue fever (Dengvaxia), a serious outcome was attributed to the vaccine: vaccinated individuals without a previous dengue infection were at increased risk of developing severe dengue if subsequently infected by a heterologous serotype. In response, the World Health Organization recommended vaccination in regions where the seroprevalence of dengue is at least 50% and, ideally, greater than 70%. Hence, accurate estimates of regional seroprevalence are crucial for both population vaccination strategies and test-then-vaccinate decisions at the individual level. Currently, estimates of seroprevalence are based on surveys, using screening tests for previous dengue exposure. These estimates must consider the sensitivity and specificity of the tests, which depend on identification of those who have been exposed, ostensibly through a test, regarded as the gold standard. There is, however, no easily accessible gold standard test for dengue. Methods: We propose an approach to estimate the seroprevalence of dengue that does not require a gold standard test by modeling: (i) the uncertainty in the sensitivity and specificity, and (ii) the uncertainty in the “true” disease prevalence. Results: Through simulations, we demonstrate the effect of these extra sources of uncertainty on post-test estimates of dengue seroprevalence. Our simulations show, for example, that in a population of 1 million it is possible to overestimate or underestimate the number who are truly seropositive by as much as 76,000. Conclusions: Current estimates can substantially overestimate or underestimate the true probability of previous exposure when these extra sources of variability are not accounted for.
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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 | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".