Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) is a respiratory viral infection caused by Severe Acute Respiratory Syndrome Corona Virus 2. The first case of the infection was confirmed in Wuhan China in 2019, by early March 2020 the infection had spread to all the continents of the World attaining a pandemic status as declared by the World Health Organization on 11th March 2020. Kenya reported its first confirmed COVID-19 case on 13th March 2020, increasing to 5206 cases as reported on 24th June 2020. COVID-19 is a novel infection with no known cure, currently, the mainstay to the infection is through public health measures. These measures are hand hygiene, cough etiquette, face masking and social distancing among others. This review aims to examine the literature on the public health measures which have been used to control outbreaks caused by respiratory viruses. The review will also identify the public health measures which Kenya is using to control the pandemic. A descriptive survey on the confirmed COVID-19 cases in Kenya shows that infection is on the rise and the epidemic curve is on the ascending trajectory. The review informs that the country requires a high level of preparedness to handle COVID-19. The areas to consider include, having robust health care systems with an adequate number of; hospital beds, healthcare workers and personal protective equipment.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".