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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".