Development of New Stringency Indices for Nonpharmacological Social Distancing Policies Implemented in Korea During the COVID-19 Pandemic: Random Forest Approach
Bibliographic record
Abstract
BACKGROUND: In the absence of an effective treatment method or vaccine, the outbreak of the COVID-19 pandemic elicited a wide range of unprecedented restriction policies aimed at mitigating and suppressing the spread of the SARS-CoV-2 virus. These policies and their Stringency Index (SI) of more than 160 countries were systematically recorded in the Oxford COVID-19 Government Response Tracker (OxCGRT) data set. The SI is a summary measure of the overall strictness of these policies. However, the OxCGRT SI may not fully reflect the stringency levels of the restriction policies implemented in Korea. Korea implemented 33 COVID-19 restriction policies targeting 4 areas: public facilities, public events, social gatherings, and religious gatherings. OBJECTIVE: This study aims to develop new Korea Stringency Indices (KSIs) that reflect the stringency levels of Korea's restriction policies better and to determine which government-implemented policies were most effective in managing the COVID-19 pandemic in Korea. METHODS: The random forest method was used to calculate the new KSIs using feature importance values and determine their effectiveness in managing daily COVID-19 confirmed cases. Five analysis periods were considered, including November 01, 2020, to January 20, 2021 (Period 1), January 20, 2021, to June 27, 2021 (Period 2), November 01, 2020, to June 27, 2021 (Period 3), June 27, 2021, to November 01, 2021 (Period 4), and November 01, 2021, to April 24, 2022 (Period 5). RESULTS: Among the KSIs, public facilities in period 4, public events in period 2, religious gatherings in periods 1 and 3, and social gatherings in period 5 had the highest importance. Among the public facilities, policies associated with operation hour restrictions in cinemas, restaurants, PC rooms, indoor sports facilities, karaoke, coffee shops, night entertainment facilities, and baths or saunas had the highest importance across all analysis periods. Strong positive correlations were observed between daily confirmed cases and public facilities, religious gatherings, and public events in period 1 of the pandemic. From then, weaker and negative correlations were observed in the remaining analysis periods. The comparison with the OxCGRT SI showed that the SI had a relatively lower feature importance and correlation with daily confirmed cases than the proposed KSIs, making KSIs more effective than SI. CONCLUSIONS: Restriction policies targeting public facilities were the most effective among the policies analyzed. In addition, different periods call for the enforcement of different policies given their effectiveness varies during the pandemic.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".