The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.
Article
Individual-Based Modelling of Animal Brucellosis Spread with the Use of Complex Networks
E. R. Pinto 1, E. G. Nepomuceno 2,*, and A. S. L. O. Campanharo 1
1 Department of Biostatistics, Institute of Biosciences São Paulo State University, Botucatu, São Paulo 18618-970, Brazil
2 Department of Electronic Engineering and Centre for Ocean Energy Research, Maynooth University, Maynooth, Ireland
* Correspondence: erivelton.nepomuceno@mu.ie
Received: 12 October 2022
Accepted: 23 November 2022
Published: 22 December 2022
Abstract: The principal purpose of this work was to study the spread of brucellosis in the state of São Paulo with the help of the complex network theory and to propose control measures for its eradication. For this, the scale-free model of complex networks, widely known in the literature, was used. The effect of vaccination was verified in each of the municipalities in the state of São Paulo and it was observed that when heterogeneity is not taken into account, vaccination becomes ineffective for the eradication of the disease.
Resumo. O propósito deste trabalho foi de estudar a propagação de doenças infecciosas com o uso do Modelo Baseado em Indivíduos (MBI) em conjunto com a teoria de redes complexas. Para isso, foram utilizados modelos de redes complexas amplamente conhecidos na literatura e analisadas as propriedades topológicas usuais das rede produzidas por tais modelos. Verificou-se o efeito topológico das mesmas na evolução de uma dada doença, e observou-se que, redes complexas com diferentes topologias resultam em curvas de indivíduos infectados com diferentes comportamentos, e desta forma, que a evolução de uma dada doençaé altamente sensívelà topologia de rede utilizada. Mais especificamente, observou-se que quanto maior o valor do comprimento do salto médio, mais rápida será a propagação de uma doença e, consequentemente, maior será o número de indivíduos infectados.
Recebido em 12 de setembro de 2018 / Aceito em 11 de fevereiro de 2020 RESUMO. O objetivo deste trabalho foi comparar a eficácia dos modelos de Programação por Metas como ferramenta de regressão não linear, com os métodos de ajustes não lineares clássicos. Aplicou-se os modelos a dados experimentais de inativação de Salmonella spp. em carne moída suína. A investigação da eficiência dos métodos foi realizada pelo cálculo do erro absoluto.
This paper presents a graphical interface called EPIGUI, which allows the study of the dynamics of an infectious disease spread using compartmental models in combination with complex networks. This interface aims at considering stochastic factors that govern the evolution of an infection in a network. Moreover, it provides simple tools to create networks of agents and to define the epidemiological parameters of outbreaks. There are six common infectious disease models (SI, SIS, SIR, SIRS, SEIR, and SEIRS) or a user can provide another model, combining compartments. Moreover, in the simulations the user can either include a synthetic network generated according to a network model (random, small-world, scale-free, modular, or hierarchical) or a real network. This approach can help understand the paths followed by outbreaks in a given community and design new strategies to prevent and to control them.
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