Spatial Modeling and Analysis of Human Traffic and Infectious Virus Spread in Community Networks

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 2286 - 2289
• Main Authors: Zhang, Siqi, Yang, Hui
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Analytical models; Biological system modeling; Computer Simulation; COVID-19; Epidemics; Epidemiology; human traffic; Humans; infectious disease; Infectious diseases; network modeling; Spatial databases; spatial network; Statistics; Traffic control; Virus Diseases - transmission
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630798

The use of network models to study the spread of infectious diseases is gaining increasing interests. They allow the flexibility to represent epidemic systems as networks of components with complex and interconnected structures. However, most of previous studies are based on networks of individuals as nodes and their social relationships (e.g., friendship, workplace connections) as links during the virus spread process. Notably, the transmission and spread of infectious viruses are more pertinent to human dynamics (e.g., their movements and interactions with others) in the spatial environment. This paper presents a novel network-based simulation model of human traffic and virus spread in community networks. We represent spatial points of interests (POI) as nodes where human subjects interact and perform activities, while edges connect these POIs to form a community network. Specifically, we derive the spatial network from the geographical information systems (GIS) data to provide a detailed representation of the underlying community network, on which human subjects perform activities and form traffics that impact the process of virus transmission and spread. The proposed framework is evaluated and validated in a community of university campus. Experimental results showed that the proposed simulation model is capable of describing interactive human activities at an individual level, as well as capturing the spread dynamics of infectious diseases. This framework can be extended to a wide variety of infectious diseases and shows strong potentials to aid the design of intervention policies for epidemic control.