COVID-19, Age and Mortality: Implications for Public Policy
Bibliographic record
Abstract
Backdrop to the PandemicIn late December 2019, authorities in Wuhan, China, were informed of a new respiratory disease infecting increasing numbers of people. 1 They, in turn, notified the World Health Organization (WHO). 2 By January 30, 2020, there were about 10,000 cases globally, and the WHO cautioned of a public health emergency of international concern 3 -a formal declaration of "an extraordinary event which is determined to constitute a public health risk to other States through the international spread of disease and to potentially require a coordinated international response." 4As of late May, there are nearly six million cases globally.COVID-19 originates from a retrovirus that entered humans as a zoonotic disease -a disease that crosses the species barrier from animal to human and then spreads from human to human. 5The earliest cases were associated with a local "wet market" in Wuhan, where live and slaughtered animals, both domestic and exotic, were being sold as food. 6COVID-19 -known officially as "severe acute respiratory syndrome coronavirus 2" or by its abbreviation, SARS-CoV-2 -spreads rapidly between humans, mainly via the respiratory tract through droplets and fomites. 7It presents through a wide range of symptoms, primarily but not restricted to fever, cough and difficulty breathing. 8As time passes, the range of symptoms identified with COVID-19 increases.Preliminary studies suggest that SARS-CoV-2's case fatality rate, at 2.2 percent, is significantly lower than that of its predecessors. 9It was 9.6 percent for SARS-CoV and 34.4 percent for MERS-CoV. 10However, the rate remains in flux as the pandemic continues to unfold.The most vulnerable are the elderly (over 65) and those with comorbidities (underlying medical conditions). 11The main comorbidities associated with poor prognosis are cancers, diabetes and hypertension, as well as respiratory, cardiac and renal diseases. 12
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.021 | 0.012 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".