Leveraging the influenza sentinel surveillance platform for SARS-CoV-2 monitoring in Bangladesh (2020–2024): a prospective sentinel surveillance study
Bibliographic record
Abstract
Background: There is limited global evidence on whether influenza sentinel surveillance platforms can be effectively adapted for long-term SARS-CoV-2 monitoring in low-resource contexts. We explored the utility of the hospital-based influenza sentinel surveillance (HBIS) platform for monitoring SARS-CoV-2 in Bangladesh by comparing SARS-CoV-2 detection in HBIS platform with national COVID-19 platform and assessing how its integration into influenza surveillance aligns with national trends. Methods: From March 2020 to December 2024, we analysed data from patients with severe acute respiratory infection (SARI) and influenza-like illness (ILI) enrolled in HBIS. Socio-demographic and clinical data were recorded, and nasopharyngeal and oropharyngeal swabs were tested for influenza and SARS-CoV-2 using rRT-PCR. Whole-genome sequencing was performed on a subset of SARS-CoV-2-positive samples. Data from national COVID-19 platform were obtained from the Directorate General of Health Services, Bangladesh, and were compared with HBIS platform data using epidemic curves and Pearson correlation analysis. Findings: = 0.86, P < 0.001). Sequencing of 234 SARS-CoV-2 strains detected the beta and delta variants in April and May 2021, respectively, and omicron subvariants circulating from 2022 to 2024, aligning with the national COVID-19 platform. Interpretation: SARS-CoV-2 positivity trends in HBIS platform closely aligned with the national COVID-19 platform, demonstrating its potential as a sustainable platform for COVID-19 monitoring. Our findings underscore the feasibility of influenza sentinel surveillance as an early warning system for future COVID-19 outbreaks or other respiratory viruses of pandemic concern in Bangladesh and similar settings. Funding: Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA (U01GH002259).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".