@article{kim2021network,

title = {Network analysis to identify the risk of epidemic spreading},

author = {Kiseong Kim and Sunyong Yoo and Sangyeon Lee and Doheon Lee and Kwang-Hyung Lee},

url = {https://www.mdpi.com/2076-3417/11/7/2997},

doi = {10.3390/app11072997},

year  = {2021},

date = {2021-03-26},

urldate = {2021-03-26},

journal = {Applied Sciences},

volume = {11},

number = {7},

pages = {2997},

publisher = {MDPI},

abstract = {Several epidemics, such as the Black Death and the Spanish flu, have threatened human life throughout history; however, it is unclear if humans will remain safe from the sudden and fast spread of epidemic diseases. Moreover, the transmission characteristics of epidemics remain undiscovered. In this study, we present the results of an epidemic simulation experiment revealing the relationship between epidemic parameters and pandemic risk. To analyze the time-dependent risk and impact of epidemics, we considered two parameters for infectious diseases: the recovery time from infection and the transmission rate of the disease. Based on the epidemic simulation, we identified two important aspects of human safety with regard to the threat of a pandemic. First, humans should be safe if the fatality rate is below 100%. Second, even when the fatality rate is 100%, humans would be safe if the average degree of human social networks is below a threshold value. Nevertheless, certain diseases can potentially infect all nodes in the human social networks, and these diseases cause a pandemic when the average degree is larger than the threshold value. These results indicated that certain infectious diseases lead to human extinction and can be prevented by minimizing human contact.},

keywords = {Disease spread, Epidemic disease, Network analysis, Pandemic},

pubstate = {published},

tppubtype = {article}

}