Positivity rate: an indicator for the spread of COVID-19
Bibliographic record
Abstract
This paper investigates the relationship between the positivity rate and numbers of deaths and intensive care unit (ICU) patients. In addition, it explores the use of the positivity rate as an indicator for the spread of COVID-19. We used COVID-19 datasets for eight countries – Canada, USA, UK, Italy, Belgium, Ireland, Colombia and South Africa – and considered two correlation cases. The first case considers the correlation of the number of confirmed cases with each of deaths and ICU patients. The second case considers the correlation of the positivity rate with each of deaths and ICU patients. When obtaining the correlation, we considered different lagging periods between the date of confirming a case and the date of its ICU admittance or death. We compared the obtained correlation coefficient values for each of the two considered cases to explore whether the positivity rate is a better indicator for the spread of the disease than confirmed cases. For each of the eight considered countries, we obtained the daily reproduction number using each of confirmed cases and positivity rate. The two obtained sets of reproduction number values for each country were statistically compared to investigate whether they are significantly different. When considering the daily positivity rate instead of the daily number of confirmed cases, the maximum correlation with deaths is increased by 349.9% for the USA (the country with the highest increase) and 4.5% for the UK (the country with the lowest increase), with an average increase of 60.8% considering the eight countries. Considering the daily positivity rate instead of the daily number of confirmed cases caused the maximum correlation with the number of ICU patients to be increased by 74.7% for the USA (the country with the highest increase) and 2.2% for the UK (the country with the lowest increase), with an average increase of 25% over the considered countries. The results for the daily reproduction number obtained using the positivity rate are statistically different from those obtained using daily confirmed cases. The results indicate that positivity rate is a better indicator for the spread of the disease than the number of confirmed cases. Therefore, it is highly advised to use measures based on the positivity rate when indicating the spread of the disease and considering responses accordingly because these measures consider the daily number of tests and confirmed cases.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".