Wavelet-based Statistical and Mathematical Analysis of Spread of COVID-19
Bibliographic record
Abstract
The outbreak of coronavirus-19 (NCoV-19) has developed a universal crisis due to high rate of infection and mortality. Therefore, the researchers are using various available methods to study the pattern of spread of COVID-19 which will help in planning to control the disease and to manage the health care resources. This study compares Autoregressive Integrated Moving Average (ARIMA) (statistical), Logistic, Gompertz (mathematical) and their hybrid using Wavelet-based Forecast (WBF) models to model and predict the number of confirmed cases of COVID-19. The study area includes the countries: Iran, Italy, Pakistan, Saudi Arabia, USA, UK and Canada. Moreover, root mean squares error (RMSE) is used to compare the performance of studied models. Empirical analysis shows that confirmed cases could be adequately modelled using ARIMA and ARIMA-WBF for all the countries under consideration. However, for future prediction significance of the models varies region to region.Keywords: COVID-19; ARIMA; Logistic; Gompertz; Wavelet-based-forecast
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".