Entropy-Based Pandemics Forecasting

被引：37
|
作者：
Lucia, Umberto [1 ]
Deisboeck, Thomas S. [2 ]
Grisolia, Giulia [1 ]
机构：
[1] Politecn Torino, Dipartimento Energia Galileo Ferraris, Turin, Italy
[2] Harvard Med Sch, Massachusetts Gen Hosp, Harvard MIT Martinos Ctr Biomed Imaging, Dept Radiol, Charlestown, MA USA
来源：
FRONTIERS IN PHYSICS | 2020年 / 8卷
关键词：
SARS-Cov-2; Covid-19; coronavirus; epidemics-pandemics; non-equilibrium statistical thermodynamics; epidemiology; POWER LAWS; ZIPFS LAW; DISTRIBUTIONS;
D O I：
10.3389/fphy.2020.00274
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
A great variety of natural phenomena follows some statistical distributions. In epidemiology, such as for the current COVID 19 outbreak, it is essential to develop reliable predictions of the evolution of an infectious disease. In particular, a statistical projection of the time of maximum diffusion of infected carriers is fundamental in order to prepare healthcare systems and organize a robust public health response. In this paper, we develop a thermodynamic approach based on the infection statistics related to the total citizenry of a country. It represents a novel tool for evaluating the time of maximum diffusion of an epidemic or pandemic.
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页数：7
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