Artificial Intelligence to Model the COVID-19 Country Infection Trends

The Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) organized an online repository (available at https://github.com/CSSEGISandData/COVID-19) with world-wide information on the absolute number of new confirmed, recovered, and death cases related to the COVID-19 disease (Coronavirus Disease 2019) caused by the Sars-CoV-2 virus (coronavirus). From the whole dataset, we have focused our analysis on the daily time series summaries, which contain the accumulated numbers of confirmed, death, and recovered cases for each country. Given some countries (e.g., Australia, Canada, and China) were reported at the province/state level, we have aggregated all those into a single time series. Another important modification in this dataset was performed to reorganize the daily records. Instead of using accumulated cases, we calculated the lagged differences between consecutive days. Besides the dataset, we also share our source code designed to cluster time series from different countries with similar behavior.

Aiming at reproducing our results, run the source code "tree-clustering.R"

Our main contribution is the function "calc.dend.dists" available in "distances-dendrogram.R" .

For more information, visit our project https://tsviz.icmc.usp.br/covid19

约翰霍普金斯大学系统工程中心（JHU CSSE）组织了一个全球信息在线数据库（可在https://github.com/CSSEGISandData/COVID-19访问），其中包含了与由Sars-CoV-2病毒（冠状病毒）引起的2019冠状病毒病（COVID-19）相关的每日新增确诊、康复和死亡病例的绝对数量。在分析整个数据集时，我们聚焦于每日时间序列的汇总，其中包含了各国累积的确诊、死亡和康复病例数。鉴于部分国家（例如澳大利亚、加拿大和中国）的数据以省/州级别报告，我们将这些数据汇总成单一的时间序列。此外，本数据集的重要修改之一在于重新组织了每日记录。我们不再使用累积病例数，而是计算了连续两天之间的滞后差异。除了数据集之外，我们还分享了用于对来自不同国家的具有相似行为的时间序列进行聚类的源代码。为了重现我们的结果，请运行源代码"tree-clustering.R"。我们的主要贡献是包含在"distances-dendrogram.R"中的函数"calc.dend.dists"。如需更多信息，请访问我们的项目https://tsviz.icmc.usp.br/covid19。