Zika virus outbreak in Suriname, a report based on laboratory surveillance data
Bibliographic record
Abstract
INTRODUCTION: Since the identification of ZIKV in Brazil in May 2015, the virus has spread extensively throughout the Americas. Cases of ZIKV infection have been reported in Suriname since October 2, 2015. METHODS: A laboratory-based surveillance system was quickly implemented according to previous experience with the emergence of chikungunya. General practitioners and public health centers located in different districts of Suriname were asked to send blood samples from suspicious cases to Academic Hospital for molecular diagnosis of Zika virus infection. We investigated Zika-related laboratory data collected during surveillance and response activities to provide the first outbreak report in Suriname in terms of time, location and person. RESULTS: A total of 791 molecularly confirmed cases were reported during a 48-week interval from October 2015 to August 2016. The majority of ZIKV-positive cases involved women between 20 and 39 years of age, reflecting concern about Zika infection during pregnancy. The outbreak peaked in mid-January and gradually spread from the district of Paramaribo to western coastal areas. DISCUSSION: This report provides a simple and comprehensive description of the outbreak in Suriname and demonstrates the utility of laboratory data to highlight the spatiotemporal dynamics of the outbreak in that 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".