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- W3180959986 abstract "Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies." @default.
- W3180959986 created "2021-07-19" @default.
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- W3180959986 date "2021-07-07" @default.
- W3180959986 modified "2023-10-18" @default.
- W3180959986 title "Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data" @default.
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- W3180959986 doi "https://doi.org/10.3390/electronics10141626" @default.
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