Trends in case-fatality rates of COVID-19 in the World, between 2019 - 2020
Bibliographic record
Abstract
Introduction: CoV infections can potentially cause from a simple cold to a severe respiratory syndrome, such as the Severe Acute Respiratory Syndrome and the Middle East Respiratory Syndrome (MERS-CoV). The COVID-19 created a new reality for global healthcare models. Objetive: To evaluate trends in case fatality rates of COVID-19 in the World. Methods: We conducted a population based time-series study using public and official data of cases and deaths from COVID-19 in Argentina, Australia, Brazil, Chile, China, Colombia, France, Germany, India, Iran, Italy, Japan, Mexico, Morocco, New Zealand, Nigeria, Peru, Saudi Arabia, South Africa, South Korea, Spain, Switzerland, United Kingdom, United States and Russian, between December, 2019 and August, 2020. Data were based on reports from European Centre for Disease Prevention and Control. COVID-19 was defined by the International Classification of Diseases, 10th revision (U07.1). A Prais-Winsten regression model was performed and the Daily Percentage Change (DPC) calculated determine rates as increasing, decreasing or flat. Results: During the study period, trends in case-fatality rates in the world were flat (DPC = 0.3; CI 95% [-0.2: 0.7]; p = 0.225). In Africa, Morocco had decreasing trends (DPC = -1.1; CI 95% [-1.5: -0.7]; p < 0.001), whereas it were increasing in South Africa (p < 0.05) and flat in Nigeria (p > 0.05). In the Americas, Argentina showed a decreasing trend in case-fatality rates (DPC = -0.6; CI 95% [-1.1: -0.2]; p = 0.005), the U.S. had flat trends (p > 0.05) and all other American countries had increasing trends (p < 0.05). In Asia, Iran had decreasing trends (DPC = -1.5; CI 95% [-2.6 : -0.2]; p = 0.019); China and Saudi Arabia showed increasing trends (p < 0.05), while in India, Japan and South Korea they were flat (p > 0.05). European countries had mostly increasing trends (p < 0.05): Germany, Italy, Spain, the UK and Russia; France and Switzerland had flat trends (p > 0.05). Finally, in Oceania, trends in case-fatality rates were flat in Australia (p > 0.05) and increasing in New Zealand (p < 0.05). Conclusion: Trends in case-fatality rates of COVID-19 in the World were flat between December, 31 and August, 31. Argentina, Iran and Morocco were the only countries with decreasing trends. On the other hand, South Africa, Brazil, Canada, Chile, Colombia, Mexico, Peru, China, Saudi Arabia, Germany, Spain, United Kingdom, Russian and New Zealand had increasing trends in case-fatality rate. All the other countries analyzed had flat trends. Based on case-fatality rate data, our study supports that COVID-19 pandemic is still in progress worldwide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".