基于拓扑自编码器的COVID-19传动系统动力学相似映射生成
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder
摘要：
于二零二零年初，全球首次爆发COVID-19（一种由SARS-CoV 2病毒在中国引起的疾病）。世界卫生组织（WHO）于2020年3月11日宣布该疾病为大流行病。随着该疾病在全球蔓延，很难追踪该疾病在所有国家的传播动态，因为这些国家在地理、人口和战略方面可能有所不同。在这篇短文中，作者提出利用一种神经网络来为这些动态生成一个全局拓扑图，其中共享相似动态的国家被相邻映射，而具有显著不同动态的国家被映射到彼此远离。作者认为，这种拓扑图可以用于进一步分析和比较疾病动态与减轻这一全球危机的战略之间的相关性，以直观的方式。本说明解释了240多个国家的患者人数时间序列的一些初步实验。
英文摘要：
At the beginning of 2020 the world has seen the initial outbreak of COVID-19, a disease caused by SARS-CoV2 virus in China. The World Health Organization (WHO) declared this disease as a pandemic on March 11 2020. As the disease spread globally, it becomes difficult to tract the transmission dynamics of this disease in all countries, as they may differ in geographical, demographic and strategical aspects. In this short note, the author proposes the utilization of a type of neural network to generate a global topological map for these dynamics, in which countries that share similar dynamics are mapped adjacently, while countries with significantly different dynamics are mapped far from each other. The author believes that this kind of topological map can be useful for further analyzing and comparing the correlation between the diseases dynamics with strategies to mitigate this global crisis in an intuitive manner. Some initial experiments with with time series of patients numbers in more than 240 countries are explained in this note.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
