ArticleExpert systems with applications2018
Disease spreading in complex networks: A numerical study with Principal Component Analysis.
Article in Expert systems with applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Simulating targeted vaccination strategies with network-based and agent-based models: A scoping review.PLOS global public health · 2026Article
- Pertinence of contact duration as edge feature for epidemic spread analysis.Scientific reports · 2025Article
- Optimizing Contact Network Topological Parameters of Urban Populations Using the Genetic Algorithm.Entropy (Basel, Switzerland) · 2024Article
- The Geographical Coexist of the Migratory Birds, Ticks, and Nairobi Sheep Disease Virus May Potentially Contribute to the Passive Spreading of Nairobi Sheep Disease.Transboundary and emerging diseases · 2023Article
- A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil.Computer methods and programs in biomedicine · 2021Article
- Viral disease spreading in grouped population.Computer methods and programs in biomedicine · 2020Article
- The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm.Computer methods and programs in biomedicine · 2020Article
- Wavelet-Like Transform to Optimize the Order of an Autoregressive Neural Network Model to Predict the Dissolved Gas Concentration in Power Transformer Oil from Sensor Data.Sensors (Basel, Switzerland) · 2020Article
- Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning.PloS one · 2020Article
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Authors and funding
2 authors.
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Abstract
Disease spreading models need a population model to organize how individuals are distributed over space and how they are connected. Usually, disease agent (bacteria, virus) passes between individuals through these connections and an epidemic outbreak may occur. Here, complex networks models, like Erdös-Rényi, Small-World, Scale-Free and Barábasi-Albert will be used for modeling a population, since they are used for social networks; and the disease will be modeled by a SIR (Susceptible-Infected-Recovered) model. The objective of this work is, regardless of the network/population model, analyze which topological parameters are more relevant for a disease success or failure. Therefore, the SIR model is simulated in a wide range of each network model and a first analysis is done. By using data from all simulations, an investigation with Principal Component Analysis (PCA) is done in order to find the most relevant topological and disease parameters.
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