Seasonality and Subnational Heterogeneity of Dengue in Bangladesh: A Descriptive Epidemiology
Bibliographic record
Abstract
Dengue imposes a substantial and shifting hospital burden in Bangladesh. Subnational, setting- and sex-specific profiles can support surge planning and targeted control. A retrospective, cross-sectional analysis of routine aggregates from the Directorate General of Health Services (DGHS), Bangladesh dengue dashboard was conducted for 2024. We computed national totals and admission-based fatality (deaths ÷ admissions × 100), and profiled divisions, city-corporation versus outside-city settings, sex, and month-wise seasonality. In 2024, there were 101,211 admissions, 575 deaths, and 100,040 discharges, yielding an in-hospital fatality of 0.57%. Divisionally, Dhaka accounted for 57% of admissions and 69% of deaths; Barishal for 8.7% of admissions and 11.1% of deaths; Chattogram for 15.3% of admissions and 9.6% of deaths; Khulna for 9.9% of admissions and 6.1% of deaths; Sylhet recorded 0 deaths with few cases. Admission-based fatality was highest in Barishal (0.73%), followed by Dhaka (0.68%) and Mymensingh (0.48%). Within-division setting patterns diverged: in Dhaka, ≈68% of admissions and ≈87% of deaths occurred inside the city-corporation, whereas in Chattogram the city-corporation contributed <2% of division totals for both admissions and deaths. By sex, males were 63% of admissions, with fatality 0.4% in males versus 0.8% in females (overall 0.6%). Seasonality showed rising admissions and deaths from July, peaks in October–November, and a decline in December; the first quarter displayed a relatively higher death-to-admission proportion. Historical context showed a COVID-era dip in 2020 (1,405 admissions) and a peak in 2023 (321,017). Bangladesh’s 2024 dengue burden is highly concentrated in Dhaka, with notable excess fatality shares in Barishal and a strong urban skew in the Dhaka city-corporation. Sex and seasonal differences, higher female fatality among admissions and early-year proportional fatality highlight the need for risk-based triage, timely referral, and targeted vector control aligned with predictable peaks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".