Regional differences in COVID-19 attack and case fatality rates in the first quarter of 2020: a comparative study
Bibliographic record
Abstract
Background and Objective: The COVID-19 (Coronavirus disease 2019) outbreak has become a public health threat all over the world. From December 31, 2019 to March 19, 2020, 146 countries were affected. Evidence on the management approaches of current COVID-19 pandemic is still limited though the numbers of affected countries are increasing as the days go by. This study was aimed at determining the attack rate (AR) and case fatality rate (CFR) of Covid-19 in six different regions around the world in the first quarter of 2020. An attempt was also made to provide an overview of the ongoing situation of COVID-19. Methods: The design of the study was mixed approach where a retrospective analysis of surveillance data of six different regions around the world were collected from COVID-19 dashboard of World Health organization, between 31 December 2019 to 19 March 2020 (Time: 2:00 pm. BST [CET: 9 am]). Besides, other different validated sources (example: Worldometer, Center for Disease Control and Prevention) were used to assess the ongoing situation regarding COVID-19. A statistical software SPSS version 26 was used to analyze the data. Results: There were a total of 207,860 confirmed cases and 8779 deaths across six different regions around the world from 31 December 2019 to 19 March 2020, with the highest AR of 9.92/100,000 population in Europe region, followed by Asia (2.7/ 100,000), Australia (1.75/100,000), North America (1.42/100,000), South America (0.23/100,000) and Africa (0.06/100,000) regions. Study results revealed statistically significant association between attack rates and the six regions of the world (p=0.002), meaning that AR varied in the regions around the world. The CFR was high in Europe region (4.81%), followed by Asia (4.06%), Africa (2.72%), South America (1.41%), Australia (1.12%), and North America (0.69%) regions. Data reviewed from different countries revealed that the highest number of cases was confirmed in the United States, followed by Spain and Italy. The findings revealed that the reported confirmed cases varied widely in different regions of the world. Conclusion: The severity and variation in -geographical distribution of COVID-19 cases and deaths suggest that urgent response from various government and public health authorities should be taken and research regarding underlying factors determining this severity should be sought for. Ibrahim Med. Coll. J. 2020; 14(2): 1-10
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".