Evidence-Based, Cost-Effective Interventions To Suppress The COVID-19 Pandemic: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background In an unparalleled global response, during the COVID-19 pandemic, 90 countries asked 3.9 billion people to stay home. Yet some countries avoided lockdowns and focused on other strategies, like contact tracing and case isolation. How effective and cost-effective are these strategies? We aimed to provide a comprehensive summary of the evidence on pandemic control, with a focus on cost-effectiveness. Methods Following PRISMA systematic review guidelines, MEDLINE (1946 to April week 2, 2020) and Embase (1974 to April 17, 2020) were searched using a range of terms related to pandemic control. Articles reporting on the effectiveness or cost-effectiveness of at least one intervention were included and grouped into higher-quality evidence (randomized trials) and lower-quality evidence (other study designs). Results We found 1,653 papers; 62 were included. Higher-quality evidence was only available to support the effectiveness of hand washing and face masks. Modelling studies indicated that these measures are highly cost-effective. For other interventions, lower-quality evidence suggested that: (1) the most cost-effective interventions are swift contact tracing and case isolation, surveillance networks, protective equipment for healthcare workers, and early vaccination (when available); (2) home quarantines and stockpiling antivirals are less cost-effective; (3) social distancing measures like workplace and school closures are effective but costly, making them the least cost-effective options; (4) combinations are more cost-effective than single interventions; (5) interventions are more cost-effective when adopted early and for severe viruses like SARS-CoV-2. For H1N1 influenza, contact tracing was estimated to be 4,363 times more cost-effective than school closures ($2,260 vs. $9,860,000 per death prevented). Conclusions A cautious interpretation of the evidence suggests that for COVID-19: (1) social distancing is effective but costly, especially when adopted late and (2) adopting as early as possible a combination of interventions that includes hand washing, face masks, ample protective equipment for healthcare workers, and swift contact tracing and case isolation is likely to be the most cost-effective strategy. Funding LP holds the Canada Research Chair in Community Approaches and Health Inequalities (CRC 950232541). This funding source had no role in the design, conduct, or reporting of the study.
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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 | 0.011 | 0.348 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".