학술논문

Modeling Human Travel and Social Contact with Multi-layer Networks for Epidemic Prediction
Document Type
Conference
Source
2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Bioinformatics and Computational Biology (ICBCB), 2021 IEEE 9th International Conference on. :71-76 May, 2021
Subject
Bioengineering
COVID-19
Epidemics
Biological system modeling
Computational modeling
Buildings
Urban areas
Sociology
human behaviors
agent-based models
multi-layer networks
epidemic modeling
public health
Language
Abstract
It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology.