Abstract
In the infection prevention and control of epidemics, isolation has always been an important means for mankind to curb the spread of the epidemic. Isolation targets not only confirmed patients, their close contacts and sub-close contacts, and other groups at risk. It is clearly impractical to isolate all the groups with risk. In this paper, we propose an isolation optimization algorithm Node-IBD maximizing influence blocking, aiming to isolate a certain percentage of these close contacts or sub-close contacts to maximize the prevention effect and curb the spread of the epidemic. The possibility of spread of the epidemic can be minimized even when potentially infected persons in the risk population cannot be identified. This paper proves the feasibility and effectiveness of the isolation algorithm through the experiments of static contact network and dynamic contact network. It is expected to provide a useful strategy for future epidemic prevention.
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Acknowledgment
This work was supported by the NSFC under Grant no. 62276171, the Natural Science Foundation of Guangdong Province of China under Grant Nos. 2019A1515011173 and 2019A1515011064, the Shenzhen Fundamental Research-General Project under Grant No. JCYJ20190808162601658, CCF- Baidu Open Fund, NSF-SZU and Tencent-SZU fund.
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Zhou, S. et al. (2023). Node-IBD: A Dynamic Isolation Optimization Algorithm for Infection Prevention and Control Based on Influence Diffusion. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_42
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DOI: https://doi.org/10.1007/978-981-99-2385-4_42
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