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Browsing by Author "Lyu, Fangzheng"

Now showing 1 - 2 of 2
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    From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread
    Song, Zhenlei; Zhang, Zhe; Lyu, Fangzheng; Bishop, Michael; Liu, Jikun; Chi, Zhaohui (MDPI, 2024-06-13)
    In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions.
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    NetPointLib: Library for Large-Scale Spatial Network Point Data Fusion and Analysis
    Kang, Yunfan; Lyu, Fangzheng; Wang, Shaowen (ACM, 2024-07-17)
    Network-constrained events, including for example traffic accidents and crime incidents, are widespread in urban environments. Understanding spatial patterns of these events within network spaces is essential for deciphering the underlying dynamics and supporting informed decision-making. The fusion and analysis of networkconstrained point data pose significant computational challenges, particularly with large datasets and sophisticated algorithms. In this context, we introduce NetPointLib, a computationally efficient library designed for processing and analyzing large-scale event data in network spaces. NetPointLib utilizes the capabilities of highperformance computing (HPC) environments including ROGER supercomputer, ACCESS resources, and the CyberGISX platform, providing a scalable and accessible framework for conducting network point data fusion and pattern analysis and supporting computational reproducibility. The library encompasses several algorithmic implementations, including the network local K function and network scan statistics, to enable researchers and practitioners to identify spatial patterns within network-constrained data. This is achieved by harnessing the computational power of HPC resources, facilitating advanced spatial analysis in an efficient and scalable manner.