Abstract
Complex networks constitute a new field of scientific research that is derived from the observation and analysis of real-world networks, for example, biological, computer and social ones. An important subset of complex networks is the biological, which deals with the numerical examination of connections/associations among different nodes, namely interfaces. These interfaces are evolutionary and physiological, where network epidemic models or even neural networks can be considered as representative examples. The investigation of the corresponding biological networks along with the study of human diseases has resulted in an examination of networks regarding medical supplies. This examination aims at a more profound understanding of concrete networks. Fuzzy logic is considered one of the most powerful mathematical tools for dealing with imprecision, uncertainties and partial truth. It was developed to consider partial truth values, between completely true and completely false, and aims to provide robust and low-cost solutions to real-world problems. In this manuscript, we introduce a fuzzy implementation of epidemic models regarding the Human Immunodeficiency Virus (HIV) spreading in a sample of needle drug individuals. Various fuzzy scenarios for a different number of users and different number of HIV test samples per year are analyzed in order for the samples used in the experiments to vary from case to case. To the best of our knowledge, analyzing HIV spreading with fuzzy-based simulation scenarios is a research topic that has not been particularly investigated in the literature. The simulation results of the considered scenarios demonstrate that the existence of fuzziness plays an important role in the model setup process as well as in analyzing the effects of the disease spread.
Highlights
Graphs are appropriate mathematical structures to represent and analyze complex networks, and graph theory is a field in mathematics that deals with the study of graphs [1].Graph theory supports the visualization and analysis of complex network structures
This paper focuses on the context of epidemics, the same model can be directly applied to many different spreading processes in complex networks
Since our motivation stems from the fact that we are interested in exploring the way that issues around virulence evolution rely on the modeled contact structure, we examine a model with growing complexity levels in the contact structure, but at the same time, rearrange several of the remaining epidemiological forms
Summary
Graphs are appropriate mathematical structures to represent and analyze complex networks, and graph theory is a field in mathematics that deals with the study of graphs [1]. Graph theory supports the visualization and analysis of complex network structures. The comprehensive knowledge of a complex network structure may contribute to the extraction of valuable information with the aim of further assessing and enhancing methodologies, tools, as well as the outcomes of shaped examinations. All of these reasons contribute to the motivation of the present work, which deals with analyzing the problem of HIV spreading in a sample of needle drug individuals
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