Estimating the Force of Infection for Dengue Virus Using Repeated Serosurveys, Ouagadougou, Burkina Faso
Bibliographic record
Abstract
RESEARCH D engue fever is a mosquitoborne disease caused by 4 related but antigenically distinct dengue viruses (DENVs), serotypes 1-4.Annually, ≈50-100 million cases of dengue are reported worldwide, with 20,000 deaths (1).Aedes mosquitoes and dengue cases were documented in Africa as early as 1823, and cases have since been reported in 34 countries in Africa (2).In Burkina Faso, since the first outbreak in 1925, there have been multiple others (2), including in 2013, 2016, and 2017 (3-5).The 2016 outbreak included 1,061 dengue rapid-diagnostic test (RDT) positive cases and 15 deaths in the capital, Ouagadougou, with a reported case-fatality rate (CFR) of 1.2% (4,6).The 2017 outbreak included 5,773 RDT-positive cases and 18 deaths throughout the country, for a CFR of 0.2% (5).These repeated outbreaks suggest a considerable dengue burden in the country.Despite this burden, data on dengue seroprevalence and force of infection (FOI), the rate at which initial or heterotypic infections are acquired, are scarce in Burkina Faso and Africa (7).In terms of seroprevalence in Burkina Faso, 1 study found the dengue IgG seroprevalence among 683 pregnant women and blood donors to be 26.3% in rural and 36.5% in urban settings in 2003-2004 (8).To define DENV transmission in Burkina Faso, we conducted 4 serologic surveys in the same study participants in Ouagadougou during 2015-2017.The study targeted 3 objectives.First, we measured seroprevalence of DENV by IgG positivity at enrollment, serosurvey 1 (S1).Second, we estimated age-specific annual FOI, measured by seroconversion in the repeated follow-up surveys ( S2-S4).Last, because a dengue outbreak occurred in 2016, between the third and fourth serosurveys, we identified and compared demographic and clinical characteristics associated with DENV seroconversion in the outbreak and nonoutbreak periods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".