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4171. Understanding the structure and dynamics of the global vaccine product trade network.
Invited abstract in session WC-6: Advancements of OR-analytics in statistics, machine learning and data science 18, stream Advancements of OR-analytics in statistics, machine learning and data science.
Wednesday, 12:30-14:00Room: 1013 (building: 202)
Authors (first author is the speaker)
| 1. | Rui Zheng
|
| Alliance Manchester Business School, University of Manchester | |
| 2. | Luis Ospina-Forero
|
| Alliance Manchester Business School-3.046, University of Manchester | |
| 3. | Yu-wang Chen
|
| Alliance Manchester Business School, The University of Manchester |
Abstract
The most economical measure for tackling public health emergencies is mass immunization. During crises, policymakers heavily rely on global human vaccine product trade as it profoundly shapes the development of additional intervention measures. Drawing on global human vaccine product trade data from 2019 to 2022, this study employs social network analysis methods to explore the structure of the global human vaccine product trade network from various network statistical features such as community structure and evolution. In addition, through a comparative examination of global monthly trade networks for human vaccines, this study reveals the evolving structural dynamics amidst the global public health crisis (COVID-19), offering insights into how it affects national intervention strategies and vaccine market stability during pandemics. Overall, the global human vaccine product trade network has experienced an increase in its scale with an extended multipolar structure. Additionally, the alterations in network structure revealed by trade volume and frequency can also demonstrate competitive dynamics and behaviors between countries, and highlight the shifts in vaccine strategies among countries during public health emergencies. High-income countries with rapid vaccine rollout and introduction (such as the United States) exhibit stronger resilience by actively adopting comprehensive intervention measures.
Keywords
- Analytics and Data Science
- Graphs and Networks
- Disaster and Crisis Management
Status: accepted
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