A Bayesian Approach to Improving Spatial Estimates After Accounting for Misclassification Bias in Surveillance Data for COVID-19 in Philadelphia, PA
Bibliographic record
Abstract
Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate. Using a Bayesian approach, we adjusted for misclassification to improve spatial estimation of COVID-19 in Philadelphia, PA at the ZIP code level. We modeled true prevalence as a function of area deprivation index and spatial random effects at the ZIP code level. There were 111,497 documented tests as of June 10, 2020 in Philadelphia, among whom 23,941 (21%) were classified by the laboratory as positive for SARS-CoV-2, translating to an observed prevalence of 1.5%. After accounting for bias in the surveillance data, the posterior citywide true prevalence was 2.8% (95% credible interval: 2.7%, 3.0%). Overall the posterior surveillance sensitivity and specificity were 58.9% (95% credible interval: 41.5%, 75.9%) and 99.8% (95% credible interval: 99.7%, 100%), respectively. Underreporting tends to understate discrepancies in burden for more affected areas, potentially leading to bias in setting priorities.
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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 | 0.066 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".