A review of analytical models and methods for COVID-19: vaccination and testing
Bibliographic record
Abstract
We survey the literature on pandemic testing and vaccination with an emphasis on analytical and operations research models and methodologies. The systematic review summarizes and categorizes the current state of research, highlights opportunities for future studies, and aspires to assist healthcare planners and researchers in using the most efficient strategies and tools. We surveyed 80 papers, 42% of which focus on vaccination strategies, and the remainder on testing. Vaccine allocation under limited supply is studied using simulation and optimization and focuses on the trade-off between coverage and efficacy, as well as on vaccine allocation among specific demographic groups. Few studies account for the effect of non-pharmaceutical interventions (NPIs), which calls for models that combine coverage, efficacy and NPIs, while capturing different subject-specific attributes and accounting for the economic impact of such strategies. Similarly, research on testing strategies covers most of the pandemic literature, uses simulation and optimization methodologies, and focuses on the trade-off between accuracy and coverage. There is a need for models accommodating diverse testing strategies, different dimensions of heterogeneity, dynamically changing prevalence rates, vaccine efficacy, and NPIs.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".