论文标题
使用拓扑自动编码器生成COVID-19传输动力学的相似性图
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder
论文作者
论文摘要
在2020年初，世界已经发生了Covid-19的最初爆发，这是一种由中国SARS-COV2病毒引起的疾病。世界卫生组织（WHO）在2020年3月11日宣布这种疾病为大流行。随着该疾病在全球范围内传播，在所有国家中，都很难散发这种疾病的传播动态，因为它们在地理，人口统计学和战略方面可能有所不同。在简短的说明中，作者建议利用一种神经网络来为这些动态生成全球拓扑图，在这些动力学中，共享相似动态的国家被相邻映射，而动力学显着不同的国家则彼此之间映射了巨大的绘制。作者认为，这种拓扑图可用于进一步分析和比较疾病动态与以直观方式减轻这一全球危机的策略之间的相关性。在本说明中解释了一些与240多个国家 /地区的时间序列患者人数的初步实验。
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.
