The Effects of Nonpharmaceutical Interventions on COVID-19 Cases, Hospitalizations, and Mortality: A Systematic Literature Review and Meta-analysis
Bibliographic record
Abstract
Introduction: To assess the effects of various non-pharmaceutical interventions (NPI) on cases, hospitalizations, and mortality during the first wave of the COVID-19 pandemic. Methods: To empirically investigate the impacts of different NPIs on COVID-19-related health outcomes, a systematic literature review was conducted. We studied the effects of 10 NPIs on cases, hospitalizations, and mortality across three periodic lags (2, 3, and 4 weeks-or-more following implementation). Articles measuring the impact of NPIs were sourced from three databases by May 10, 2022, and risk of bias was assessed using the Newcastle-Ottawa scale. Results: Across the 44 papers, we found that mask wearing corresponded to decreased per capita cases across all lags (up to -2.71 per 100,000). All NPIs studied except business and bar/restaurant closures corresponded to reduced case growth rates in the two weeks following implementation, while policy stringency and travelling restrictions were most effective after four. While we did not find evidence of reduced deaths in our per capita estimates, policy stringency, masks, SIPOs, limited gatherings, school and business closures were associated with decreased mortality growth rates. Moreover, the two NPIs studied in hospitalizations (SIPOs and mask wearing) showed negative estimates. Conclusions: When assessing the impact of NPIs, considering the duration of effectiveness following implementation has paramount significance. While some NPIs may reduce the COVID-19 impact, others can disrupt the mitigative progression of containing the virus. Policymakers should be aware of both the scale of their effectiveness and duration of impact when adopting these measures for future COVID-19 waves.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".