Epidemiological models with awareness in complex networks

• Grant number: 15/26616-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2016
• End date: April 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Francisco Aparecido Rodrigues
• Grantee: Gyordano Gadoni Reis
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Epidemics propagation models are fundamental to prevent how an infectious agent spreads in a society. In the literature, different models were proposed, such as the susceptible-infected-susceptible and the susceptible-infected-recovered. Most models reckons the disease spreads in a society with no reaction from its individuals. However, together with the infectious agent, the epidemic's information also spreads in this society. Therefore, recently, a wide variety of researches were proposed to model this epidemiologic model with alert. In this project, we aim to study those models and verify how the network's topology, as well as the alert propagation methods affect the final portion of infected individuals. This study is essential to understand how information's politics can be developed to lessen an epidemicEpidemics propagation models are fundamental to prevent how an infectious agent spreads in a society. In the literature, different models were proposed, such as the susceptible-infected-susceptible and the susceptible-infected-recovered. Most models reckons the disease spreads in a society with no reaction from its individuals. However, together with the infectious agent, the epidemic's information also spreads in this society. Therefore, recently, a wide variety of researches were proposed to model this epidemiologic model with alert. In this project, we aim to study those models and verify how the network's topology, as well as the alert propagation methods influence the final portion of infected individuals. This study is essential to understand how information's politics can be developed to lessen an epidemic impact's impact.