Seroprevalence and COVID-19 deaths in Indian Cities
Bibliographic record
Abstract
Population-based sero-epidemiological studies are widely used to estimate the proportion of a population infected (infection attack rate, IAR) with SARS-CoV-2. However, the accuracy of the estimates relies on the design of the study (e.g. sample size) and the sensitivity (e.g. decay of sensitivity) of the assay used. This study aims to resolve these issues with the seroprevalence of COVID-19 and infection attack rates in 12 Indian cities as examples. We examine serological data that used Abbott to reconstruct a sensitivity decay function and use it to infer attack rates and seroprevalence based on reported COVID-19 death in these cities. We find that the reconstructed seroprevalence matched with the reported scenario reasonably well in most cities, where Abbott or similar assay was likely used, but failed in two cities, where non-Abbott assay was likely used. We propose an approach to connect the serological data and the reported COVID-19 deaths with the testing sensitive decay function to increase the confidence in estimating the size of the epidemic.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".