Covid-19 Fatality Rate in Third World Countries: A Review of Environmental Challenges and Impacts on Public Health and Human Security
Bibliographic record
Abstract
The novel coronavirus is far from being over; with the case-fatality rate (CFR) hitting more than 16,500 globally as of July, there is a worry that despite the fact that the global CFR curve is showing signs of flattening, the environmental peculiarities of the third world countries may be abetting global efforts towards containing the virus. Therefore, this review x-rayed these peculiarities in the light of their current concern in public health as per their contribution to the persistent surge in CFR in most developing nations. Given that the virus is transmitted via droplets, the review focused on how the state of public and environmental challenges such as air as well as water pollution and personal hygiene could be abetting the surge in coronavirus infections and morbidity. The review revealed, among other things, that challenges associated with poor sanitary conditions, lack of potable water, unventilated environments, air pollution, and poor inter-personal hygiene are devastating challenges in the fight against the pandemic. The implication is that since these conditions are systematic in nature, it may take more than average effort and public sacrifice to checkmate the case-fatality rate of the virus in the third world. Therefore, call for studies is necessary to establish empiricism for CFR patterns and ratio across areas in deplorable environmental and sanitary conditions.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".