Epidemic modelling by ripple-spreading network and genetic algorithm
type: Item
instance of: scholarly article
title: Epidemic modelling by ripple-spreading network and genetic algorithm
zbMATH Open document ID: 1299.92001
DOI: 10.1155/2013/506240
author: Jian-Qin Liao
author: Xiao-Bing Hu
author: Mark S. Leeson
author: Ming Wang
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals' physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.
Mathematics Subject Classification ID: 92-08
Mathematics Subject Classification ID: 92D30
zbMATH DE Number: 6372428
Wikidata QID: Q59028267
describes a project that uses: Genocop
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/506240
OpenAlex ID: W2085989605
cites work: Daniel Bernoulli's epidemiological model revisited
cites work: Q4101645
cites work: A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods
cites work: Complex networks: structure and dynamics
cites work: Epidemic Modelling using Sars as a Case Study
cites work: Ripple-spreading network model optimization by genetic algorithm
cites work: Q4079017
cites work: Q4692508
cites work: Q4315824
cites work: Introduction to evolutionary computing
description: scientific article; zbMATH DE number 6372428