An Overview of COVID-19 and Its Progression in Ghana
Bibliographic record
Abstract
This research focuses on the progression of the coronavirus pandemic in Ghana, measures put in place to fight the pandemic and evaluation of Ghana’s response in terms of both containing the pandemic and mitigating the social and economic effects of the COVID 19 pandemic. Methods: The study mainly assessed the COVID-19 situation in Ghana within the period of March 2020 to MAY 2021. Data from reputable sources; Ministry of Health, Goggle scholar, Ghana Health Service, CDC, WHO, WTTC and online news articles were retrieved and assessed in quarterly basis. The results were further tabulated and graphically represented using Microsoft Excel application. Results: A total of 94011 cases were recorded by the end of the of May 2021; first quarter of the second year. The highest number of active cases (11897), deaths (295) and critical cases (280) recorded were from December 2020 to February 2021. In the first quarter, the infection rate recorded was 3.77% which increased to 16.10% in the second quarter. However, with reinforcement of the COVID-19 protocol there was a significant decrease in infection rate in the final quarter for the studies; from March to May 2021 (3.60%). Conclusion: Actions adopted by the Ghanaian government so far in handling the pandemic have generated significant achievements. It is however recommended that more control measures such as mass vaccination, mass testing and contact tracing will help track the infection and further reduce the rate of infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".