Institute of Computing, Federal University of Bahia, Salvador, Brazil.
Department of Reproductive Biology, National Center for Child Health and Development Research Institute, Tokyo, Japan.
Sci Rep. 2021 Jul 27;11(1):15271. doi: 10.1038/s41598-021-94661-z.
COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries' movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.
COVID-19 已在全球范围内广泛传播，除了社会在未来几年将面临的间接损失外，还对多个国家的卫生系统造成了影响。尽管国家之间的比较对于控制这种疾病至关重要，但主要的挑战是各国并非同时受到病毒的影响。因此，我们使用人工智能，根据约翰霍普金斯大学系统科学与工程中心的 COVID-19 数据集，对各国的新病例和死亡人数进行了时间序列分析。我们的方法使用层次聚类逐步对病例进行建模，该聚类强调了国家随时间在感染群体之间的转变。然后，人们可以将一个国家的当前状况与已经经历过前几波疫情的国家进行比较。通过使用我们的方法，我们设计了一个转移指数来估计最有可能在感染群体之间移动的国家，以预测下一波疫情的趋势。我们得出两个重要结论：（1）我们展示了特定国家的历史感染路径，并强调了当国家在病例数较少、中等或较多的群体之间转移时发生的变化点；（2）我们使用转移指数估计特定国家的新一波疫情。
