Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies
Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies
复制标题
实时更新动态社交网络以制定 COVID-19 疫苗接种策略
DOI：
10.1101/2021.03.11.21253356
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Cheng S
中科院分区：
文献类型：
--
作者：
Cheng S
疫苗接种战略对抗击COVID-19大流行至关重要。由于许多国家的供应仍然有限，基于接触网络的干预措施可能是通过确定高风险个人或社区来制定有效战略的最有力手段。然而，由于网络的高维性，在实际应用中只能得到部分的和有噪声的网络信息，特别是在接触网络时变较大的动态系统中。此外，SARS-CoV-2的大量突变对感染概率有重大影响，需要实时网络更新算法。在这项研究中，我们提出了一种基于数据同化技术的序列网络更新方法，以结合不同来源的时间信息。然后，我们优先考虑从同化网络中获得的高度或高中心性的个体进行疫苗接种。在SIR模型中，基于同化的方法与标准方法（基于部分观察到的网络）和随机选择策略在疫苗接种有效性方面进行了比较。数值比较首先使用在一所高中收集的真实世界的面对面动态网络进行，然后根据Barabasi-Albert模型模拟多个社区的大规模社会网络生成顺序多层网络。
Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.