A review of big data analytics models for assessing non-pharmaceutical interventions for COVID-19 pandemic management
Bibliographic record
Abstract
Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".