Control of COVID-19 Pandemic: Vaccination Strategies Simulation under Probabilistic Node-Level Model
العنوان: | Control of COVID-19 Pandemic: Vaccination Strategies Simulation under Probabilistic Node-Level Model |
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المؤلفون: | Chun-Lin Kuo, Wai Kin Victor Chan, Mengxuan Chen |
المصدر: | 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). |
بيانات النشر: | IEEE, 2021. |
سنة النشر: | 2021 |
مصطلحات موضوعية: | Random graph, 0303 health sciences, Theoretical computer science, Small-world network, Social network, Computer science, business.industry, Probabilistic logic, Interval (mathematics), medicine.disease, 01 natural sciences, Contagious disease, 03 medical and health sciences, 0103 physical sciences, medicine, Benchmark (computing), 010306 general physics, business, 030304 developmental biology, Network model |
الوصف: | This paper aims at constructing a probabilistic node-level time-dependent contagious disease spreading model for coronavirus disease (COVID-19) pandemic which is called SEINRVseinr by introducing exposed and asymptomatic infectious state, imperfect vaccination, reinfected possibility and weighted undirected graph for social network into the traditional probabilistic node-level Susceptible-Infectious-Recovered (SIR) network model. This paper simulates the effectiveness of five vaccination strategies (including random base, degree-target base, random acquaintance, first-neighbor and second neighbor strategies) in random network, small world network and scale-free network. Compared with the benchmark model, the results show that random acquaintance strategy is efficient strategy and neighbors’ strategies perform better in certain interval. |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::45aa9b362b5ca28410e99c89bdca2587 https://doi.org/10.1109/icsp51882.2021.9408970 |
حقوق: | OPEN |
رقم الأكسشن: | edsair.doi...........45aa9b362b5ca28410e99c89bdca2587 |
قاعدة البيانات: | OpenAIRE |
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