Optimal strategies for controlling the MERS coronavirus during a mass gathering
Bibliographic record
Abstract
1 A two-group model for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is de2 signed and used to assess the impact of quarantine of susceptible individuals and a hypothetical 3 anti-MERS-CoV vaccine on the transmission dynamics of MERS-CoV within the two groups. 4 The model undergoes a backward bifurcation, which is shown to arise due to the assumption 5 that the hypothetical vaccine offers incomplete protection against infection. The model can 6 have one or more endemic equilibria when the associated reproduction number exceeds unity. 7 Uncertainty and sensitivity analyses are carried out to determine the effect of uncertainties in 8 the parameter estimates of the model, as well as to determine the main parameters that drive 9 the disease transmission process. The model is re-formulated as an optimal control problem, 10 and the resulting model is used to evaluate the impact of various control strategies. Numerical 11 simulations of the optimal control model suggest that if the cost of implementing quarantine and 12 vaccination strategies are high, the two strategies can be administered optimally by using their 13 maximum feasible (coverage) levels for a relatively shorter period of time (i.e., “hit-hard and 14 hit-early”), and the coverage level then continuously decreased the next few days afterwards. 15 Furthermore, a universal strategy, based on the combined use of quarantine and vaccination 16 strategies, is shown to be more effective than the singular implementation of either the vaccina17 tion or quarantine strategy. 18
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".