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 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".