Clinical profiles and hematologic patterns in Bangladesh's 2019 dengue outbreak
Bibliographic record
Abstract
During the 2019 monsoon and post-monsoon seasons, Bangladesh experienced an unprecedented nationwide dengue outbreak, which, for the first time, spread to all sixty-four districts. This study aimed to analyze this outbreak's clinical and hematological features to understand the disease's progression and geographic variation in symptom presentation. We conducted a retrospective analysis of 1875 laboratory-confirmed dengue cases, including 432 children (23.0 %) and 1443 adults (77.0 %), aged 1–67 years. Clinical features were assessed with a focus on geographic variation, and hematologic indices, e.g., white blood cell (WBC) count and platelet count , were monitored throughout the illness. Fever was universally observed, serving as the cardinal symptom of dengue infection. Gastrointestinal complications were the second most frequent category of symptoms, with abdominal pain (55.9 %) and nausea and/or vomiting (47.5 %) being most common. Hemorrhagic symptoms were reported in approximately one-third of the participants. Additionally, 75.6 % and 60.1 % of participants experienced thrombocytopenia and leukocytopenia , respectively, with WBC and platelet counts sharply declining after symptom onset and gradually recovering by the second week. Participants from Dhaka reported significantly more gastrointestinal symptoms , e.g., abdominal pain and nausea/vomiting, compared to those from other districts, indicating geographic variation in clinical presentation. This study provides a comprehensive overview of the clinical and hematological profiles seen during the 2019 dengue outbreak in Bangladesh. It highlights the impact of geographic factors on clinical presentation and the need for customized management strategies to enhance dengue care and outbreak preparedness in similar settings.
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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 | 0.000 |
| 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".