Author: Danillo Barros de Souza; Fernando A N Santos; Everlon Figueiroa; Jailson B Correia; Hernande P da Silva; Jose Luiz de Lima Filho; Jones Albuquerque
Title: Using curvature to infer COVID-19 fractal epidemic network fragility and systemic risk Document date: 2020_4_6
ID: a47l7m47_8
Snippet: In this paper, we create an epidemic network consisting of edges and links, based on the reported epidemic time series. We define each spatial domain of the epidemic as the node of a network, and the links between two locations are based on the Pearson correlation coefficient (or any similarity measure) between their epidemic time-series. We chose the links of the network according to the Pearson correlation coefficient between two locations in d.....
Document: In this paper, we create an epidemic network consisting of edges and links, based on the reported epidemic time series. We define each spatial domain of the epidemic as the node of a network, and the links between two locations are based on the Pearson correlation coefficient (or any similarity measure) between their epidemic time-series. We chose the links of the network according to the Pearson correlation coefficient between two locations in descending order, which means that we include the strongest links first in the network, until the network reaches the Giant components (the state in which we have a single cluster of connected nodes).
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