Zimbabwe in the COVID-19 Era, a Critical Reflection on Third World Nation's Management of COVID-19
Bibliographic record
Abstract
The coronavirus has been recorded as one of the most deadliest virus of the 21 st century after the spanish swine flu of the 1940s in the category of flus and the worst pandemic of 1918 that killed approximately 50 million people wolrd wide.The corona virus take 2 -14 days and in some cases 0 -24 days to present itself as well as trigger recovery or futility.However compared to other forms of influenza, other relatively related virus, the discrepancy with SARS is 2 -7 days, which can extend to 10, MERS takes 5 days to present but can extent from 2 -14 days, swine flu presets in 1 -4 days that can extend to 7 days and lastly seasonal flu which presents in 2 days but ranging from 1 -4 days [1, 2].Therefore the research seeks to establish the current status on corona virus projection in Zimbabwe after implementation of lockdown statutes.Assessing the trend of local cases in Zimbabwe after implementation of lockdown statutes.This further extends into evaluating how the strategies effected have influenced differences between and within provinces of Zimbabwe.And lastly explore on the support structures available institutionally, within the districts, province and national"s level of effectiveness. MATERIALS AND METHODSTrends in corona virus statistics across the globe have largely contributed to classification of the pathology from laboratory confirmed to clinical confirmed cases were the two methodologies have seen differential diagnosis leading to spike in the number of new cases as of February 12 th 2020 [3].The COVID-19 pandemic has ravaged the world this year virtually disturbing "normal" lives.It is now common knowledge that the world is navigating through a deadly COVID-19 pandemic with statistics of confirmed corona virus cases worldwide as at 03 September 2020 at 25, 842, 652 confirmed cased and 858, 629 deaths as compared to 19,279,077 and Deaths at: 718,024 as at 7 August 2020 [4].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".