Piecewise linear modelling and change-point analysis of COVID-19 outbreak in Malaysia
Bibliographic record
Abstract
Abstract In Malaysia, COVID-19 were first detected as imported cases on 25 January and as local infection on 4 February 2020. A surge of positive cases ensued by March 2020 which led to a series of countrywide containment and mitigation measures known as Movement Control Order (MCO). We study the direct effects of MCO on the course of epidemic by analyzing the cumulative and daily infection cases of COVID-19 up to 31 December 2020 in Malaysia and its states using piecewise linear regression and segment neighborhoods algorithm of change-point analysis, respectively. Through piecewise regression on nationwide cases, MCO were likely to almost flatten the epidemic curve in just one month after it was first initiated. While for stateswise cases, the average length of series of concave downward is six months before it turn to concave upward, indicating the period of which deceleration of new cases can be expected. However, the starting of this wave of COVID-19 can be relatively vary for three months in different states and federal territories. Together with change-point analysis on daily cases, the statewise epidemic phases could be subdivided into two to four regimes, whereby the majority of phase transitions fall in April and last quarter of 2020. Overall, the statistical modelling shows that the immediate effect of MCO appears to be effective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".