Modelling and estimation of COVID-19 pandemic with the face mask and vaccination
Bibliographic record
Abstract
Abstract COVID-19 is a public health emergency for human beings and brings some very harmful consequences in social and economic fields. In order to model COVID-19 and develop the effective controlling measures, this paper proposes an SEIR-type epidemic model with the mask wearing and the vaccination. Firstly, the effective reproduction number and the threshold conditions are obtained by analyzing the dynamical behaviors of the proposed epidemic model. Secondly, selecting the data of South Korea from January 20, 2022 to March 21, 2022, all model parameters are defined and estimated. Finally, based on the estimated parameters, the numerical simulations are conducted and the simulation suitably fit with the presented model. The results show that the mask wearing ratio, the effectiveness of the certain face mask, the vaccination rate and effectiveness of vaccination for the susceptible individuals play an important role in preventing and controlling COVID-19 pandemic. The face mask wearing is associated with 83% and 90% reductions in the numbers of the cumulative cases and the newly confirmed cases respectively after sixty days while the face mask wearing rate increases 15%, and the vaccination rate is associated with 75% and 80% reductions in the numbers of the cumulative cases and the newly confirmed cases respectively after sixty days while the vaccination rate augments 15\%. Therefore, the effect of the mask wearing on reducing the cumulative cases and the newly confirmed cases is more remarkable than that of the vaccination, this means the disease control departments should strongly recommended that peoples should wear the face mask to prevent themselves from becoming infected when the vaccination willingness of individuals is relative low. The face mask wearing still the primary measure to prevent and control the transmission of COVID-19 pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".