In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Cheng:2021:10.1101/2021.03.11.21253356,
author = {Cheng, S and Pain, CC and Guo, Y-K and Arcucci, R},
doi = {10.1101/2021.03.11.21253356},
title = {Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies},
url = {http://dx.doi.org/10.1101/2021.03.11.21253356},
year = {2021}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title><jats:p>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.</jats:p>
AU - Cheng,S
AU - Pain,CC
AU - Guo,Y-K
AU - Arcucci,R
DO - 10.1101/2021.03.11.21253356
PY - 2021///
TI - Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies
UR - http://dx.doi.org/10.1101/2021.03.11.21253356
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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