Bibliographic record
Abstract
The emergence of new drug can stop Ebola and cure patients whose disease is not advanced. It optimizes the eradication of Ebola, or at least its current strain. For the sake of dealing with this problem, there are three models being developed. Firstly, this paper establishes the model 1 on the basis of the classical model of SIR and diffusion characteristic of Ebola virus. It verifies the reduction of the spread of the virus, the improvement of the patient’s cure rate and the effectiveness of three preventive measures which are significant in the formation of herd immunity. At the same time, we use linear programming to control the cost of drug delivery. Model 2, namely, the model of SIR with pulse vaccination, provides a pulse vaccination therapy on the basis of model 1. Model 2 considers many factors comprehensively, such as the cycle of inoculation, vaccination rate, the birth rate, death rate and so on. We use differential equation models to get the critical condition of the number of susceptible people, vaccination rate, and the development of predicated estimate with the change of time. Next, based on the model 2, we establish model 3 which not only considers many factors comprehensively, such as the amount of supply, the location of supply and so on, but also introduce 0-1 variable to combine the general linear programming with another linear programming which is not fixed but multi objectives so that we get the drug delivery network. Meanwhile, this paper obtains the best drug delivery program which has to spend the minimum cost on the condition of effectively controlling the epidemic. Also the result can alleviate serious situation of the Ebola virus epidemic diffusion through the drug delivery network. This paper puts forward the improvement of the model by using the Self-Organizing Map neural network and cluster analysis to get the urgent degree of different epidemic areas and divide these areas into different priorities. We get a goal programming model based on different priority. Furthermore, we use the drug delivery model and Lingo to get a more reliable drug delivery program on the basis of the objective function of priority.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".