We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and su...We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion,although not in a uniform way or timing.Despite this diversity,we find that the reported fatality cases grow following a power law in all European countries we studied.The difference among countries is the value of the power-law exponent 3.5<α<8.0.This common attribute can prove a practical diagnostic tool,allowing reasonable predictions for the growth rate from very early data at a country level.We propose a model for the disease-causing interactions,based on a mechanism of human decisions and risk taking in interpersonal associations.The model describes the observed statistical distribution and contributes to the discussion on basic assumptions for homogeneous mixing or for a network perspective in epidemiological studies of COVID-19.展开更多
文摘We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion,although not in a uniform way or timing.Despite this diversity,we find that the reported fatality cases grow following a power law in all European countries we studied.The difference among countries is the value of the power-law exponent 3.5<α<8.0.This common attribute can prove a practical diagnostic tool,allowing reasonable predictions for the growth rate from very early data at a country level.We propose a model for the disease-causing interactions,based on a mechanism of human decisions and risk taking in interpersonal associations.The model describes the observed statistical distribution and contributes to the discussion on basic assumptions for homogeneous mixing or for a network perspective in epidemiological studies of COVID-19.
