Employing Fuzzy Logic to Analyze the Structure of Complex Biological and Epidemic Spreading Models

Nickie Lefevr, Andreas Kanavos, Vassilis C. Gerogiannis, Lazaros Iliadis and Panagiotis Pintelas
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Nickie Lefevr: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
Andreas Kanavos: Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
Vassilis C. Gerogiannis: Department of Digital Systems, Geopolis Campus, University of Thessaly, 41500 Larissa, Greece
Lazaros Iliadis: Department of Civil Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Panagiotis Pintelas: Department of Mathematics, University of Patras, 26500 Patras, Greece

Mathematics, 2021, vol. 9, issue 9, 1-23

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.

Keywords: fuzzy models; complex networks; biological networks; neural networks; epidemic models; Acquired Immunodeficiency Syndrome (AIDS); Human Immunodeficiency Virus (HIV) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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