Browsing by Author "Luo, Xiaofeng"

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    A hierarchical intervention scheme based on epidemic severity in a community network
    (Springer, 2023-07) He, Runzi; Luo, Xiaofeng; Asamoah, Joshua Kiddy K.; Zhang, Yongxin; Li, Yihong; Jin, Zhen; Sun, Gui-Quan; 0000-0002-7066-246X
    As there are no targeted medicines or vaccines for newly emerging infectious diseases,isolation among communities (villages, cities, or countries) is one of themost effectiveintervention measures. As such, the number of intercommunity edges (NIE) becomesone of themost important factor in isolating a place since it is closely related to normallife. Unfortunately, how NIE affects epidemic spread is still poorly understood. In this paper, we quantitatively analyzed the impact of NIE on infectious disease transmissionby establishing a four-dimensional SIR edge-based compartmental model with two communities. The basic reproduction number R0( l ) is explicitly obtained subjectto NIE l . Furthermore, according to R0(0) with zero NIE, epidemics spreadcould be classified into two cases. When R0(0) > 1 for the case 2, epidemics occurwith at least one of the reproduction numbers within communities greater than one,and otherwise when R0(0) < 1 for case 1, both reproduction numbers within communitiesare less than one. Remarkably, in case 1, whether epidemics break out stronglydepends on intercommunity edges. Then, the outbreak threshold in regard to NIEis also explicitly obtained, below which epidemics vanish, and otherwise break out.The above two cases form a severity-based hierarchical intervention scheme for epidemics.It is then applied to the SARS outbreak in Singapore, verifying the validityof our scheme. In addition, the final size of the system is gained by demonstrating theexistence of positive equilibrium in a four-dimensional coupled system. Thoretical results are also validated through numerical simulation in networks with the Poisson and Power law distributions, respectively. Our results provide a new insight into controlling epidemics.
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    The relationship between clustering and networked Turing patterns
    (Chaos, 2024-07) Luo, Xiaofeng; Sun, Guiquan; He, Runzi; Asamoah, Joshua Kiddy K.; Xue, Yakui; Chang, Lili; 0000-0002-7066-246X
    Networked Turing patterns often manifest as groups of nodes distributed on either side of the homogeneous equilibrium, exhibiting high and low density. These pattern formations are significantly influenced by network topological characteristics, such as the average degree. However, the impact of clustering on them remains inadequately understood. Here, we investigate the relationship between clustering and networked Turing patterns using classical prey–predator models. Our findings reveal that when nodes of high and low density are completely distributed on both sides of the homogeneous equilibrium, there is a linear decay in Turing patterns as global clustering coefficients increase, given a fixed node size and average degree; otherwise, this linear decay may not always hold due to the presence of high-density nodes considered as low-density nodes. This discovery provides a qualitative assessment of how clustering coefficients impact the formation of Turing patterns and may contribute to understanding why using refuges in ecosystems could enhance the stability of prey–predator systems. The results link network topological structures with the stability of prey–predator systems, offering new insights into predicting and controlling pattern formations in real-world systems from a network perspective.