Modeling the effectiveness of the PSBB based on COVID-19 case in Greater Surabaya Area
Bibliographic record
Abstract
Abstract East Java province with high mobility has a high case fatality rate of COVID-19. The core spread of COVID-19 is from the Greater Surabaya area following Surabaya, Sidoarjo, and Gresik districts. The East Java Government through Regulation No.18/2020 imposed a Large-Scale Social Restriction (PSBB) that is intended to support the effectiveness of the physical distancing strategy in addressing the emergency status of the COVID-19. But no official report has been found on the effectiveness of PSBB. Therefore, it is necessary to evaluate the effectiveness of PSBB, especially in Greater Surabaya. This research aims to know the model of PSBB policy to minimize the spread of COVID-19 in the greater Surabaya. The study focused on health facility (ventilator, ICU, non-ICU), population, case over a certain period, and positive case in care. This study analyzes the distribution pattern and models the effectiveness of PSBB against the spread of COVID-19 in Greater Surabaya. The data analysis used the COVID-19 Surge-CDC Model. The result of the research shows that the condition of COVID-19 cases increased significantly in the model without intervention. The sharp increase in cases is related to the anticipation of other policies related to the ability of regions to provide health facilities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".