Characterizing dengue seroprevalence and heterogeneities in transmission intensity in Ghana
Bibliographic record
Abstract
There has been no confirmed case of dengue in Ghana to-date, and the risk of infection is unknown. This is largely on account of limited dengue surveillance in the country. To determine the historical circulation of dengue and reconstruct the immunity profile of the population, we conducted an age-stratified seroprevalence study using archival samples obtained from a representative SARS-CoV-2 serosurvey in three major cities. An Enzyme-Linked Immunosorbent Assay (ELISA) was used to measure IgG levels of dengue serotypes 1-4. A subset of samples was also tested by ELISA NS1 dengue antigen (n=200) and Plaque Reduction Neutralization Test (PRNT) for all 4 dengue serotypes (n=69). We used a Bayesian approach to reconstruct all results obtained in the study and estimate the force of infection of dengue assuming a time-constant transmission. 1486 plasma samples were tested from Kumasi (n= 477), Accra (n= 490), and Tamale (n= 519). The estimated sensitivity and specificity of the IgG ELISA assay compared to the PRNT were respectively 83% (95% CrI 78-88) and 89% (95% CrI 80-97), while the NS1assay had a sensitivity of 20% (95% CrI 12-30), and a specificity of 99% (95% CrI 94-1). We estimated large heterogeneities in dengue transmission intensity across locations. A higher average annual per-capita risk of dengue infection was estimated in Tamale [0.071 (95% CrI 0.056-0.096)] compared to Accra [0.026 (95% CrI 0.021-0.033)] and Kumasi [0.005 (95% CrI 0.001-0.008)]. On average, we estimated that respectively 43%, 11%, and 70% of the Accra, Kumasi, and Tamale populations have been exposed to dengue. This study provides evidence that dengue has been circulating at different endemic levels across Ghana, with higher circulation in locations close to Burkina Faso, which has recorded Africa's largest dengue outbreaks to date. There is hence the need for enhanced passive and active surveillance to monitor the circulation and potential emergence of dengue outbreaks in the country.
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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 | 0.001 |
| 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) | 0.001 | 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".