Investigating the Trajectory of the COVID-19 Outbreak in Milwaukee County and Projected Effects of Relaxed Distancing.
Bibliographic record
Abstract
INTRODUCTION: The coronavirus pandemic has placed enormous stresses on health care systems across the United States and internationally. Predictive modeling has been an important tool for projecting utilization rates and surge planning. As the initial outbreak begins to slow, questions are being raised regarding long-term coronavirus mitigation plans. This paper examines the current status of the coronavirus outbreak in Milwaukee County, Wisconsin, and simulates several scenarios where physical distancing measures are removed. METHODS: The outbreak's doubling time, reproductive numbers at several points, and incidence curve were calculated to assess outbreak progression. Compartmental models were used to estimate the number of hospitalizations and critically ill patients in Milwaukee County if distancing policies were removed. RESULTS: The compartmental models predict a substantial spike in cases and overwhelming medical resource utilization with an abrupt end to social distancing. Partial reduction in social distancing policies would likely result in a smaller spike, with less severe strain on available medical resources. CONCLUSIONS: Milwaukee County remains very susceptible to a resurgence of COVID-19 cases. Removing physical distancing policies poses significant risks with regard to resource management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".