Preparing for Disease X requires CLARITY – Lessons learned from COVID-19 – A systematic Review
Bibliographic record
Abstract
Abstract Objectives The COVID-19 pandemic has highlighted the critical need for comprehensive preparedness strategies for future pandemics, particularly those caused by unknown pathogens, or Disease X. Study Design Our systematic review examined current literature on COVID-19 risk factors, identifying significant gaps and inconsistencies in data reporting. Methods A systematic literature search was conducted including all English language published retrospective and prospective observational studies which documented clinical outcomes of COVID-19 in adult patients, on Ovid MEDLINE(R) and Epub databases (December 2019 to March 2023). Results The search yielded 440 articles of which 29 were included in the systematic review. We identified major risk factors for severe outcomes. However, inconsistencies in reporting comorbidities, age, disease control levels, medication use, vaccination status, and variant type, limited our analysis. In many publications, this information was not included. Additionally, variations in public attitudes, knowledge, and behaviours regarding COVID-19 vaccines further complicated resource distribution. We propose the CLARITY model to address these gaps by standardizing risk factor reporting, encompassing detailed comorbidity profiles, disease control metrics, age analyses, medication and vaccination data, and variant-specific information. Conclusions Implementing the CLARITY model could improve preparedness and response strategies for Disease X, enabling healthcare professionals and systems to allocate resources more effectively and mitigate the impact of future pandemics. This review underscores the necessity for a coordinated global effort to enhance pandemic preparedness through improved data reporting and risk stratification. Funding This study received financial support from Moderna Biopharma Canada Corporation. Trial Registration PROSPERO Identifier CRD42023449647
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 | 0.034 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".