Pathogen diversity in meta-population networks

Nie, Yanyi; Zhong, Xiaoni; Lin, Tao; Wang, Wei

doi:10.1016/j.chaos.2022.112909

Cited by 24 publications

(4 citation statements)

References 57 publications

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“…Also, in the study process of transmission dynamics, besides the existence of pairwise interactions, there are often higher-order interactions, and migration between group communities affects the disease transmission process. Based on the analysis of the studies by Nie et al [47] and Nie et al, [48] in the future, we will try to build a better model to take into account the influence of these factors.…”

Section: Discussionmentioning

confidence: 99%

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Huo 霍,

Wu 武

2024

Chinese Phys. B

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Nowadays, with the rapid development of the Internet, a large amount of information often accompanies the rapid transmission of disease outbreaks, and more and more scholars are studying the relationship between information and the disease transmission process on the basis of complex networks. In fact, the disease transmission process is very complex, beside the information of this factor, often will be individual behavioral measures and other factors together, most of the previous research to establish a two-layer network model to consider the impact of information on the transmission process of disease, divided into information and behavior respectively on the transmission of diseases less research, in order to more in-depth analyze disease transmission process and the intrinsic influence mechanism, this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamics co-evolution of information diffusion, vaccination behavior, and disease transmission is proposed considering four influential relationships between adjacent layers in multilayer networks. In the information layer, the diffusion process of negative information is described, and the feedback effects of the local and global vaccination are considered; in the behavioral layer, the individual vaccination behavior is described, and the probability of individual receiving vaccination behavior is influenced by two factors:one is the influence of negative information, and the other is the influence of local and global disease severity; in the disease layer, individual susceptibility is considered to be influenced by vaccination behavior. The state transition equations are derived by using the Micro Markov Chain Approach (MMCA), and disease prevalence thresholds are obtained. It was demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior, and is mainly influenced by global vaccination behavior; the vaccination behavior is mainly influenced by local disease conditions, is less influenced by global disease conditions; the disease transmission threshold increases with increasing vaccination rate; the scale of disease transmission increases with increasing negative information diffusion rate and decreases with increasing vaccination rate. Finally, it is found that when individual vaccination behavior considers both the influence of negative information and disease, it can increase the disease transmission threshold and reduce the scale of disease transmission, so we should resist the diffusion of negative information, increase vaccination proportion, and take appropriate protective measures in time.

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Section: Discussionmentioning

confidence: 99%

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Huo 霍,

Wu 武

2024

Chinese Phys. B

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“…A prime illustration of this phenomenon was observed during the transformation of Vibrio cholerae from non-pathogenic to pathogenic variants, the process of which entails the acquisition of the type IV toxinco-regulated pilus (TCP) followed by infection with the filamentous phage CTXϕ, exploiting pilus as an entry point and introducing genes coding the production of cholera toxin. Current studies strongly indicate that specific distributed genes and gene combinations play crucial roles in determining which kinds of strains are most likely to act as pathogens [73][74][75]. The most prevalent pathogen genes included RpoB (belonging to Acinetobacter baumannii), followed by RSc0454 and speC (Ralstonia solanacearum), as well as CcoN2 and rmlA (Pseudomonas aeruginosa).…”

Section: (4) Corynebacteriaceae Familymentioning

confidence: 99%

Metagenomic and Culturomics Analysis of Microbial Communities within Surface Sediments and the Prevalence of Antibiotic Resistance Genes in a Pristine River: The Zaqu River in the Lancang River Source Region, China

Yan,

Xu,

Huang

et al. 2024

Microorganisms

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Microbial communities inhabiting sedimentary environments in river source regions serve as pivotal indicators of pristine river ecosystems. While the correlation between antibiotic resistome and pathogenicity with core gut bacteria in humans is well established, there exists a significant knowledge gap concerning the interaction of antibiotic resistance genes (ARGs) and human pathogenic bacteria (HPB) with specific microbes in river source basins, often referred to as “terrestrial gut”. Understanding the microbial composition, including bacteria and resident genetic elements such as ARGs, HPB, Mobile Genetic Elements (MGEs), and Virulence Factors (VFs), within natural habitats against the backdrop of global change, is imperative. To address this gap, an enrichment-based culturomics complementary along with metagenomics was conducted in this study to characterize the microbial biobank and provide preliminary ecological insights into profiling the dissemination of ARGs in the Lancang River Source Basin. Based on our findings, in the main stream of the Lancang River Source Basin, 674 strains of bacteria, comprising 540 strains under anaerobic conditions and 124 under aerobic conditions, were successfully isolated. Among these, 98 species were identified as known species, while 4 were potential novel species. Of these 98 species, 30 were HPB relevant to human health. Additionally, bacA and bacitracin emerged as the most abundant ARGs and antibiotics in this river, respectively. Furthermore, the risk assessment of ARGs predominantly indicated the lowest risk rank (Rank Ⅳ) in terms of endangering human health. In summary, enrichment-based culturomics proved effective in isolating rare and unknown bacteria, particularly under anaerobic conditions. The emergence of ARGs showed limited correlation with MGEs, indicating minimal threats to human health within the main stream of the Lancang River Source Basin.

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“…As early as two decades ago, the problem of percolation on complex networks attracted the attention of a large number of scientists 1,2,[4][5][6][7][8]10 . Initially, the problem of percolation on a simple network was studied, and the dynamic process of cascading failure of the network was analyzed step by step.…”

Section: Inroductionmentioning

confidence: 99%

Message passing approach to analyze the robustness of hypergraph

Peng¹,

Qian²,

Zhao³

et al. 2023

Preprint

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Hypergraph networks are closer to real life because they can reflect higher-order interactions, so researchers have begun using them to build models for real-world networks. The mean-field approach is the current tool for studying the percolation problem on hypergraph networks. However, we found that when there is a loop in the hypergraph network, the calculated results using this approach deviate from the real results. Therefore, in this paper, we rephrase the percolation on the hypergraph network as a message passing process, thus obtaining a message passing approach. Our proposed approach has been tested in several hypergraph networks with loops, and the experimental results are more accurate than those under the mean-field approach. This is helpful to analyze and understand the robustness of hypergraph networks with loops. In addition, we also specifically analyzed how four different types of loops affect the accuracy of the experiment. Our proposed message passing approach also provides another way to study percolation on hypergraph networks.

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Pathogen diversity in meta-population networks

Cited by 24 publications

References 57 publications

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

Metagenomic and Culturomics Analysis of Microbial Communities within Surface Sediments and the Prevalence of Antibiotic Resistance Genes in a Pristine River: The Zaqu River in the Lancang River Source Region, China

Message passing approach to analyze the robustness of hypergraph

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