Concurrent impact evaluation of lockdown measures on COVID-19 positivity in three states of India
Bibliographic record
Abstract
Background: In response to the COVID pandemic, many preventive steps have been undertaken in the India, including lockdown measures. The objective of the study was to analyze the impact of lockdown on COVID-19 epidemic.Methods: We used quasi-experimental interrupted time series analysis using reported data from 17 March 2020 to 14 April 2020 with effective time interruption on 3 April 2020. We used publicly available data from three states to calculate the pre and post lockdown period COVID-19 test positivity.Results: The lockdown was able to reduce the infections cases in all three states. The trend of positivity changed to negative for Tamil Nadu and Odisha and accelerated upward in Kerala. The trend changes for positivity were statistically significant for two states (Tamil Nadu and Odisha). In comparison to counterfactual, on 13 April 2020, the predicted relative change in COVID-19 positivity was maximum for the state of Odisha (108%), followed by Tamil Nadu (85%) and Kerala (78%) respectively.Conclusions: The lockdown measurements were observed to be effective in the three states studied. However, the quantity of change varied from state to state. Policymakers and public health scientists can consider these findings ad methodology for future action.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".