Comparative Analysis of Continuous Transfer Function Modeling and ARMAX Models for COVID-19 Spread Prediction
Bibliographic record
Abstract
The COVID-19 pandemic has profoundly affected global health, economies, and everyday life, highlighting the importance of precise predictive models for its spread.This paper emphasizes the use of Continuous Transfer Function models and ARMAX models to simulate and forecast the transmission dynamics of COVID-19, utilizing data from Romania.The comparative analysis between Continuous Transfer Function modelling and ARMAX models for predicting the spread of COVID-19 involves evaluating both methodologies' capabilities, strengths, and limitations.This detailed examination highlights how each approach handles the complexities of epidemiological data and their effectiveness in forecasting disease dynamics.The study utilizes data from Romania to validate and compare the two models.By analysing the transmission dynamics of COVID-19 in a specific region, the study provides concrete examples of each model's performance.The comparative analysis of Continuous Transfer Function modelling and ARMAX models for COVID-19 spread prediction offers valuable insights into the strengths and limitations of each approach.In the case of the first models excel in capturing detailed, continuous-time dynamics but require more complex data and implementation.In contrast, ARMAX models provide simpler, robust short-term forecasts using more accessible discrete-time data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".