COVID-19 test positivity rate dynamics in West Sumatra, Indonesia: a retrospective study
Bibliographic record
Abstract
Abstract Background: COVID-19 test positivity rate (TPR) is essential to estimate and control SARS-CoV-2 transmission in a population at a specific time, yet the TPR trends at a provincial level in Indonesia are unclear. This study aimed to determine the COVID-19 TPR dynamics of the Indonesian West Sumatra province in the first year of documented cases.Methods: We conducted a retrospective study using secondary data of the COVID-19 quantitative reverse transcription-polymerase chain reaction (q-RT-PCR) test in West Sumatra Province from April 2020 to March 2021. To examine trends, we estimated TPR(s) on an annual, quarterly, and monthly basis in the province, its regions (cities/ regencies), and districts.Results: From a total of 410,424 individuals taking the COVID-19 q-RT-PCR examination during one year, the provincial TPR was 8.11%. The third quarter (October 2020 – December 2020, 12.18%) and October 2020 (15.62%) had the highest TPR quarterly and monthly, respectively. The TPR of cities was almost certainly twice that of regencies. Annual TPR varied significantly (p<0.001) across regions, districts, and periods.Conclusion: The COVID-19 TPR trends in West Sumatra at the first year of the pandemic were generally higher than the global recommendation. Further study on population density, public mobility, and implementation of health protocol in the province should be valuable to understand TPR dynamics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".