Node-Immunization Strategies in a Stochastic Epidemic Model

The object under study is an epidemic spread of a disease through individuals. A stochastic process is first introduced, inspired in classical Susceptible, Infected and Removed (SIR) model. In order to jeopardize the epidemic spread, two different immunization strategies are proposed. A combinatoria...

Full description

Saved in:
Bibliographic Details
Published inMachine Learning, Optimization, and Big Data Vol. 9432; pp. 222 - 232
Main Authors Piccini, Juan, Robledo, Franco, Romero, Pablo
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2016
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Algorithms & data structures
Artificial intelligence
Combinatorial optimization problem
Epidemic model
Infected and removed model
Information retrieval
Stochastic process
Susceptible
Online AccessGet full text

Cover

More Information
Summary:The object under study is an epidemic spread of a disease through individuals. A stochastic process is first introduced, inspired in classical Susceptible, Infected and Removed (SIR) model. In order to jeopardize the epidemic spread, two different immunization strategies are proposed. A combinatorial optimization problem is further formalized. The goal is to minimize the effect of the disease spread, choosing a correct immunization strategy, subject to a budget constraint. We are witness of a counter-intuitive result: in non-virulent scenarios, it is better to immunize common individuals rather than communicative ones. A discussion is provided, together with open problems and trends for future work.
ISBN:3319279254
9783319279251
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-27926-8_19