Complex network analysis of arboviruses in the same geographic domain: Differences and similarities

Santos, Eslaine S.; Miranda, José García Vivas; Saba, Hugo; Skalinski, Lacita Menezes; Araújo, Márcio Luís Valença; Veiga, Rafael Valente; Costa, Maria da Conceição Nascimento; Cardim, Luciana Lobato; Paixão, Enny S.; Teixeira, Maria Glória; Andrade, Roberto F. S.; Barreto, Maurício Lima

doi:10.1016/j.chaos.2023.113134

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Based on complex network theory, accurately mining influential nodes in the network helps us better control the dissemination of rumor 1 , inhibit the outbreak of epidemics 2 , optimize the plan of advertising 3 , predict popular research results 4 , prevent diseases outbreaks 5 , and so on.…”

Section: Introductionmentioning

confidence: 99%

HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model

Zhao,

Ye,

2024

Preprint

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In recent years, the proliferation of major power network security incidents triggered by malicious attacks has drawn widespread attention to influential nodes in power networks. Among the existing influential node identification algorithms, the global algorithms are too complex and not suitable for large-scale networks. The local algorithms are too simply to ignore the differentiated features of the same node in different information dissemination processes. To overcome these shortcomings, this paper proposes a novel influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model. The concepts of network directionalization strategy and hub-triggered neighborhood decomposition are introduced to distinguish the functional differences between nodes in the virus-spreading process. Moreover, this paper proposes the concepts of infected potential and infecting potential, and constructs a calculation model with asymmetric characteristics based on the order-by-order recurrence method to make full use of the information in the connection structure of the adjacent neighborhood. Finally, the influence of the hub node is evaluated by integrating the infected potential and infecting potential of neighbors of multiple orders. Experimental results show that the algorithm proposed in this paper outperforms the other six algorithms in terms of SIR correlation coefficient, imprecision function and algorithm resolution.

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Section: Introductionmentioning

confidence: 99%

HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model

Zhao,

Ye,

2024

Preprint

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“…For instance, the complex networks approach was one of the tools used to study the recent COVID-19 pandemic [5,6]. However, this approach was used many times before to study the spreading of diseases such as HIV [7], influenza [8][9][10], Ebola [9], Zika [9,11,12], chikungunya [11,12] and dengue [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Complexity in the dengue spreading: A network analysis approach

Lima,

Atman

2023

PLoS ONE

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In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector—the Aedes aegypti mosquito—which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q–Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.

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A Review and Analysis of Computational Approaches in Diagnosing, Monitoring, Controlling, and Developing Treatments and Vaccines Against the Zika Virus

De Assis,

Vianna,

Cardoso

et al. 2024

IEEE Access

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Complex network analysis of arboviruses in the same geographic domain: Differences and similarities

Cited by 3 publications

References 43 publications

HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model

HA: An influential node identification method based on hub-triggered neighborhood decomposition and asymmetric order-by-order recurrence model

Complexity in the dengue spreading: A network analysis approach

A Review and Analysis of Computational Approaches in Diagnosing, Monitoring, Controlling, and Developing Treatments and Vaccines Against the Zika Virus

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