Analytical models and methods for the COVID-19 pandemic: a survey focused on progression and mitigation <i>via</i> non-pharmaceutical interventions
Bibliographic record
Abstract
The ongoing global impact of the COVID-19 pandemic necessitates a complex, multistage approach involving containment, suppression and mitigation. We review analytical and operations research (OR) work dedicated to forecasting pandemic progression and devising non-pharmaceutical interventions (NPIs), with a particular focus on these topics due to their recent prominence and practical importance. This systematic review aims to provide decision-makers and healthcare planners with the most effective tools and motivate further focused and impactful research. A total of 79 papers were surveyed, 48% of which focus on pandemic prediction and the remainder on NPIs. Diverse predictive methods, including compartmental models, statistical tools and machine learning, showed remarkable accuracy in predicting pandemic spread and in assessing the effectiveness of NPIs. There is, however, no accepted best prediction method even though ensemble forecasting appears to outperform other methods. Furthermore, NPIs are recognized for their effectiveness in mitigating the pandemic and their efficacy is influenced by factors such as location, input parameters, and demographics. These findings underscore the necessity for a comprehensive comparison of predictive methods and for models combining predictive methods and optimization strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".