Disease spreading in complex networks: A numerical study with Principal Component Analysis

Abstract

a b s t r a c tDisease 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.

A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil

“…Cellular automata have been used for studying many diseases, such as dengue [10] , [34] , Chagas disease [46] , foot and mouth disease in feral pigs [12] , bubonic plague [23] , and hepatitis B [57] , with some of these models considering some heterogeneities in the spatial distributions of the population. Also, in [41] , ordinary differential equations were a good approach for heterogeneous population models based on a wide range of complex random networks. Here, probabilistic cellular automata with a randomly varying neighbourhood could replicate the first days of the COVID-19 spreading.…”

The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm

“…The states of all individuals are simultaneously updated in the end of each time step. Similar epidemic models based on probabilistic CA were already proposed [23] , [28] , [29] , [30] ; however, this is the first one that takes into consideration the role of immune individuals in the spreading of a contagious disease.…”

A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering

“…When a correlation exists between input variables, it will increase the information redundancy, and then increase the running time of the model (Yin et al, 2021c). To eliminate the correlation, dimensionality reduction provides a practical way, such as principal component analysis (PCA) (Schimit and Pereira, 2018). The detailed procedure of PCA is introduced as follows:…”