TY - GEN
T1 - Epidemiological Model of COVID-19 based on Evolutionary Game Theory
T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
AU - Nishihata, Yu
AU - Liu, Ziang
AU - Nishi, Tatsushi
N1 - Funding Information:
The authors would like to thank the funding provided by JSPS KAKENHI KIBAN (B) 22H01714.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the prevalence of COVID-19 infection, the use of mathematical models for infectious diseases has attracted considerable attention. In a previous study, human behavioral strategies are represented using evolutionary game theory and integrated with the SIR model of the COVID-19 epidemic. However, actual COVID-19 infection has an incubation period. In addition, due to viral mutations, the number of infected people is higher in the second and subsequent epidemics than in the first one. In this study, the previous study that uses evolutionary game theory to represent human behavioral selection in the SIR model is extended to the SEIR model. Then, considering the viral mutations, the relationship between the number of infected people and the risk of infection is formulated. The simulation results indicate that, by increasing the infection rate as the infection spread, the maximum number of infected people at each infection peak continued to increase until the maximum number of simultaneously infected people is reached. This finding indicates that the number of infected people is affected by the higher infection rate caused by the virus mutation.
AB - With the prevalence of COVID-19 infection, the use of mathematical models for infectious diseases has attracted considerable attention. In a previous study, human behavioral strategies are represented using evolutionary game theory and integrated with the SIR model of the COVID-19 epidemic. However, actual COVID-19 infection has an incubation period. In addition, due to viral mutations, the number of infected people is higher in the second and subsequent epidemics than in the first one. In this study, the previous study that uses evolutionary game theory to represent human behavioral selection in the SIR model is extended to the SEIR model. Then, considering the viral mutations, the relationship between the number of infected people and the risk of infection is formulated. The simulation results indicate that, by increasing the infection rate as the infection spread, the maximum number of infected people at each infection peak continued to increase until the maximum number of simultaneously infected people is reached. This finding indicates that the number of infected people is affected by the higher infection rate caused by the virus mutation.
KW - COVID-19
KW - Evolutionary game theory (EGT)
KW - SEIR model
UR - http://www.scopus.com/inward/record.url?scp=85146357753&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146357753&partnerID=8YFLogxK
U2 - 10.1109/IEEM55944.2022.9989989
DO - 10.1109/IEEM55944.2022.9989989
M3 - Conference contribution
AN - SCOPUS:85146357753
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 686
EP - 690
BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
PB - IEEE Computer Society
Y2 - 7 December 2022 through 10 December 2022
ER -
