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Real-time updating of dynamic social networks for COVID-19 vaccination strategies.
Cheng, Sibo; Pain, Christopher C; Guo, Yi-Ke; Arcucci, Rossella.
  • Cheng S; London, UK Data Science Instituite, Department of Computing, Imperial College London.
  • Pain CC; London, UK Department of Earth Science and Engineering, Imperial College London.
  • Guo YK; London, UK Data Science Instituite, Department of Computing, Imperial College London.
  • Arcucci R; London, UK Data Science Instituite, Department of Computing, Imperial College London.
J Ambient Intell Humaniz Comput ; : 1-14, 2023 Mar 30.
Article in English | MEDLINE | ID: covidwho-2293327
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Topics: Vaccines Language: English Journal: J Ambient Intell Humaniz Comput Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Topics: Vaccines Language: English Journal: J Ambient Intell Humaniz Comput Year: 2023 Document Type: Article