Modelling for The COVID-19 with The Contacting Distance
Bibliographic record
Abstract
Abstract The COVID-19, which belongs to the family of Coronaviridae and is large-scale outbreak in the whole world, is a public health emergency for human beings and brings some very harmful consequences in social and economic fields. In order to modelling the COVID-19 and develop the efficient control method corresponding to the contacting distance, this paper proposes an SEIR-type epidemic model with the contacting distance between the healthy individuals and the asymptomatic or symptomatic infected individuals, and the immigration rate of the healthy individuals, since the contacting distance and the immigration rate are two critical factors which determine the transmission of the COVID-19. Firstly, the threshold contacting distance and the threshold immigration rate are obtained by analyze the dynamical behaviors of the proposed SEIR-type epidemic model in order to control the COVID-19. The effect of the contacting distance and the immigration rate on the control of the COVID-19 are revealed based on ecological and epidemiological issues. The results show that the COVID-19 will be controlled while the contacting distance between the healthy individuals and the symptomatic infected individuals is larger than the threshold value A~ and the immigration rate is smaller than the threshold value ~ d. Secondly, the sensitivity analysis is conducted and the results show that the contacting distance and the immigration rate play an important role in controlling the COVID-19. Finally, the numerical test for Wuhan city are conducted and the conclusions show that the extinct lag decreases as the the contacting distance increase or the immigration rate decrease. Our study could give some reasonable suggestions for the health officials and the public.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".