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• W3127007035 abstract "COVID-19 has irreversibly upended the course of human life and compelled countries to invoke national emergencies and strict public guidelines. As the scientific community is in the early stages of rigorous clinical testing to come up with effective vaccination measures, the world is still heavily reliant on social distancing to curb the rapid spread and mortality rates. In this work, we present three optimization strategies to guide human mobility and restrict contact of susceptible and infective individuals. The proposed strategies rely on well-studied concepts of network science, such as clustering and homophily, as well as two different scenarios of the SEIRD epidemic model. We also propose a new metric, called contagion potential, to gauge the infectivity of individuals in a social setting. Our extensive simulation experiments show that the recommended mobility approaches slow down spread considerably when compared against several standard human mobility models. Finally, as a case study of the mobility strategies, we introduce a mobile application, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MyCovid</i> , that provides periodic location recommendations to the registered app users." @default.
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• W3127007035 date "2021-01-01" @default.
• W3127007035 modified "2023-09-25" @default.
• W3127007035 title "Leveraging Network Science for Social Distancing to Curb Pandemic Spread" @default.
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• W3127007035 doi "https://doi.org/10.1109/access.2021.3058206" @default.
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