“第四十七章 预测新冠病毒传染性”版本间的差异
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* [http://aigraph.cslt.org/courses/47/course-47.pptx 课件] | * [http://aigraph.cslt.org/courses/47/course-47.pptx 课件] | ||
* 小清爱提问：人工智能如何预测新冠病毒传染性 ？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247487279&idx=1&sn=d6c7bc8ea1a45a6dfdcfae3ad2c6dc15&chksm=c30805edf47f8cfbac027694f266151284f08efc784718ad225ab33be85067ef37d62e93fec9&scene=178#rd] | * 小清爱提问：人工智能如何预测新冠病毒传染性 ？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247487279&idx=1&sn=d6c7bc8ea1a45a6dfdcfae3ad2c6dc15&chksm=c30805edf47f8cfbac027694f266151284f08efc784718ad225ab33be85067ef37d62e93fec9&scene=178#rd] | ||
| + | * 小清爱提问：人工智能如何预测新冠疫情？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485425&idx=1&sn=f4e19de5736a475584063a7abedb1a8f&chksm=c3080d33f47f8425da5b03161bac48aeea4eccd4bb9b6b4d74e13a31e7b9f02bc7fd9557d233&scene=178#rd] | ||
==扩展阅读== | ==扩展阅读== | ||
| − | * AI100问：人工智能如何预测新冠病毒传染性 ？ [http://aigraph.cslt.org/ | + | * AI100问：人工智能如何预测新冠病毒传染性 ？ [http://aigraph.cslt.org/ai100/AI-100-113-人工智能预测新冠病毒传染性.pdf] |
| − | * AI100问：人工智能如何预测新冠疫情 [http://aigraph.cslt.org/ | + | * AI100问：人工智能如何预测新冠疫情 [http://aigraph.cslt.org/ai100/AI-100-24-人工智能如何预测新冠疫情.pdf] |
| − | + | * 维基百科：2019 新型冠状病毒[http://aigraph.cslt.org/courses/47/2019冠状病毒病.pdf] | |
| + | * 全球新冠疫情数据 [https://ourworldindata.org/explorers/coronavirus-data-explorer] | ||
| + | * 新冠疫情：人工智能算法能“听咳嗽声音辨识新冠病毒”[http://aigraph.cslt.org/courses/47/BBC_人工智能算法能听咳嗽声音辨识新冠病毒.pdf] | ||
| + | * 2021年人工智能将在抗疫中再显身手 [https://developer.aliyun.com/article/774983] | ||
| + | * 人工智能技术在疫情中的五大应用 [https://www.51cto.com/article/707725.html] | ||
==视频展示== | ==视频展示== | ||
| + | * AI for COVID-19 [http://aigraph.cslt.org/courses/47/AI-for-COVID.mp4] | ||
| + | * MIT: detecting COVID by cough [http://aigraph.cslt.org/courses/47/AUDIO_ COVID.mp4] | ||
| 第22行： | 第29行： | ||
==开发者资源== | ==开发者资源== | ||
| − | * 新冠病毒传染性预测程序源码 [https://github.com/broadinstitute/pyro-cov] | + | * Pango 命名法 [https://cov-lineages.org/index.html] |
| − | + | * GISAID dataset [https://gisaid.org/about-us/mission/] | |
| + | * 新冠病毒传染性预测程序源码 [*][https://github.com/broadinstitute/pyro-cov] | ||
==高级读者== | ==高级读者== | ||
| − | + | * Jake Epstein , A CDC graph shows just how different the Omicron wave is compared to previous COVID-19 surges [*][https://www.businessinsider.com/cdc-graph-shows-difference-between-omicron-variant-previous-coronavirus-surges-2022-1] | |
| − | * Jake Epstein , A CDC graph shows just how different the Omicron wave is compared to previous COVID-19 surges [https://www.businessinsider.com/cdc-graph-shows-difference-between-omicron-variant-previous-coronavirus-surges-2022-1] | + | |
* Obermeyer F, Jankowiak M, Barkas N, et al. Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness[J]. Science, 2022, 376(6599): 1327-1332. [https://www.science.org/doi/epdf/10.1126/science.abm1208] | * Obermeyer F, Jankowiak M, Barkas N, et al. Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness[J]. Science, 2022, 376(6599): 1327-1332. [https://www.science.org/doi/epdf/10.1126/science.abm1208] | ||
| + | * Vaishya R, Javaid M, Khan I H, et al. Artificial Intelligence (AI) applications for COVID-19 pandemic[J]. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020, 14(4): 337-339. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195043/] | ||
| + | * Zhou Y, Wang F, Tang J, et al. Artificial intelligence in COVID-19 drug repurposing[J]. The Lancet Digital Health, 2020, 2(12): e667-e676. [https://www.sciencedirect.com/science/article/pii/S2589750020301928] | ||
| + | * Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls[J]. AI & society, 2020, 35(3): 761-765. [https://link.springer.com/article/10.1007/s00146-020-00978-0] | ||
2023年8月13日 (日) 02:46的最后版本
教学资料
扩展阅读
- AI100问：人工智能如何预测新冠病毒传染性 ？ [3]
- AI100问：人工智能如何预测新冠疫情 [4]
- 维基百科：2019 新型冠状病毒[5]
- 全球新冠疫情数据 [6]
- 新冠疫情：人工智能算法能“听咳嗽声音辨识新冠病毒”[7]
- 2021年人工智能将在抗疫中再显身手 [8]
- 人工智能技术在疫情中的五大应用 [9]
视频展示
演示链接
开发者资源
高级读者
- Jake Epstein , A CDC graph shows just how different the Omicron wave is compared to previous COVID-19 surges [*][14]
- Obermeyer F, Jankowiak M, Barkas N, et al. Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness[J]. Science, 2022, 376(6599): 1327-1332. [15]
- Vaishya R, Javaid M, Khan I H, et al. Artificial Intelligence (AI) applications for COVID-19 pandemic[J]. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020, 14(4): 337-339. [16]
- Zhou Y, Wang F, Tang J, et al. Artificial intelligence in COVID-19 drug repurposing[J]. The Lancet Digital Health, 2020, 2(12): e667-e676. [17]
- Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls[J]. AI & society, 2020, 35(3): 761-765. [18]
