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Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-04-03 , DOI: arxiv-2004.01481 Pitoyo Hartono
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-04-03 , DOI: arxiv-2004.01481 Pitoyo Hartono
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.
更新日期：2020-07-17
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