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_1
Snippet: Epidemic outbreaks represent a significant concern for global health. Currently, the COVID-19 outbreak has caught the attention of researchers worldwide due to its rapid spread, high fluctuation in the incubation time and uncertain health and economic outcomes. One of the most urgent challenges of this outbreak concerns the development of a coordinated and continuous data-driven feedback system that could quantify the spread and the risk of the e.....
Document: Epidemic outbreaks represent a significant concern for global health. Currently, the COVID-19 outbreak has caught the attention of researchers worldwide due to its rapid spread, high fluctuation in the incubation time and uncertain health and economic outcomes. One of the most urgent challenges of this outbreak concerns the development of a coordinated and continuous data-driven feedback system that could quantify the spread and the risk of the epidemic, without strongly depending on parameter estimation and even when data is heterogeneous or subject to noise. Such a data-driven system would allow to develop adequate responses at different scales (global, national or local) and allocate limited resources in the most effective ways.
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