Estimating the Force of Infection for Dengue Virus Using Repeated Serosurveys, Ouagadougou, Burkina Faso
Bibliographic record
Abstract
Because of limited data on dengue virus in Burkina Faso, we conducted 4 consecutive age-stratified longitudinal serologic surveys, ≈6 months apart, among persons 1-55 years of age, during June 2015-March 2017, which included a 2016 outbreak. The seroconversion rate before the serosurvey enrollment was estimated by binomial regression, taking age as the duration of exposure, and assuming constant force of infection (FOI) over age and calendar time. We calculated FOI between consecutive surveys and rate ratios for potentially associated characteristics based on seroconversion using the duration of intervals. Among 2,897 persons at enrollment, 66.3% were IgG-positive, and estimated annual FOI was 5.95%. Of 1,269 enrollees participating in all 4 serosurveys, 438 were IgG-negative at enrollment. The annualized FOI ranged from 10% to 20% (during the 2016 outbreak). Overall, we observed high FOI for dengue. These results could support decision-making about control and preventive measures for dengue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".