Coordination of a Dual-Channel Pharmaceutical Supply Chain Based on the Susceptible-Infected-Susceptible Epidemic Model
Bibliographic record
Abstract
With the continuous development of Internet, online pharmaceutical channels in many countries have seen rapid expansion. As a result, pharmaceutical supply chain participants can adopt dual channels, namely, both online channels and offline channels. As online channels compete with traditional offline channels, it is of great relevance to study the potential conflicts and coordination between them, which is the focus of this paper. Specifically, this article develops a susceptible-infected-susceptible epidemic model of the dual channels for a pharmaceutical supply chain. Our main findings are that in a competitive situation, there is a positive stable equilibrium. Furthermore, increasing the rate of influence of offline transmission, online transmission, and cross transmission will improve sales. Moreover, improving the transmission influence rate will turn more potential customers into purchasers, increase channel sales, and achieve dual channel coordination. We then conduct numerical analysis to illustrate and complement the findings from the model. Finally, we provide managerial insights for implementing successful dual-channel pharmaceutical supply chains.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".