Using the Novel Mortality-Prevalence Ratio to Evaluate Potentially Undocumented SARS-CoV-2 Infection: Correlational Study
Bibliographic record
Abstract
BACKGROUND: The high prevalence of COVID-19 has resulted in 200,000 deaths as of early 2020. The corresponding mortality rate among different countries and times varies. OBJECTIVE: This study aims to investigate the relationship between the mortality rate and prevalence of COVID-19 within a country. METHODS: We collected data from the Johns Hopkins Coronavirus Resource Center. These data included the daily cumulative death count, recovered count, and confirmed count for each country. This study focused on a total of 36 countries with over 10,000 confirmed COVID-19 cases. Mortality was the main outcome and dependent variable, and it was computed by dividing the number of COVID-19 deaths by the number of confirmed cases. RESULTS: The results of our global panel regression analysis showed that there was a highly significant correlation between prevalence and mortality (ρ=0.8304; P<.001). We found that every increment of 1 confirmed COVID-19 case per 1000 individuals led to a 1.29268% increase in mortality, after controlling for country-specific baseline mortality and time-fixed effects. Over 70% of excess mortality could be attributed to prevalence, and the heterogeneity among countries' mortality-prevalence ratio was significant (P<.001). Further, our results showed that China had an abnormally high and significant mortality-prevalence ratio compared to other countries (P<.001). This unusual deviation in the mortality-prevalence ratio disappeared with the removal of the data that was collected from China after February 17, 2020. It is worth noting that the prevalence of a disease relies on accurate diagnoses and comprehensive surveillance, which can be difficult to achieve due to practical or political concerns. CONCLUSIONS: The association between COVID-19 mortality and prevalence was observed and quantified as the mortality-prevalence ratio. Our results highlight the importance of constraining disease transmission to decrease mortality rates. The comparison of mortality-prevalence ratios between countries can be a powerful method for detecting, or even quantifying, the proportion of individuals with undocumented SARS-CoV-2 infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".