Epidemic model with isolation in multilayer networks
| Autor: | Lidia A. Braunstein, L. G. Alvarez Zuzek, Harry Eugene Stanley |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Physics - Physics and Society
Complex Netorks Epidemic Models Isolation (health care) Computer science Ciencias Físicas FOS: Physical sciences Physics and Society (physics.soc-ph) Otras Ciencias Físicas Models Biological 01 natural sciences Article Isolation period 010305 fluids & plasmas law.invention purl.org/becyt/ford/1 [https] Influenza A Virus H1N1 Subtype law Influenza Human 0103 physical sciences Statistics Humans Epidemics 010306 general physics Multilayer Networks Multidisciplinary Percolation Influenza a purl.org/becyt/ford/1.3 [https] Transmission (mechanics) Epidemic model CIENCIAS NATURALES Y EXACTAS |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET Scientific Reports |
| Popis: | The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the isolation of infected individuals is disregarded. Hence we focus our study in an epidemic model in a two-layer network, and we use an isolation parameter to measure the effect of isolating infected individuals from both layers during an isolation period. We call this process the Susceptible-Infected-Isolated-Recovered ($SI_IR$) model. The isolation reduces the transmission of the disease because the time in which infection can spread is reduced. In this scenario we find that the epidemic threshold increases with the isolation period and the isolation parameter. When the isolation period is maximum there is a threshold for the isolation parameter above which the disease never becomes an epidemic. We also find that epidemic models, like $SIR$ overestimate the theoretical risk of infection. Finally, our model may provide a foundation for future research to study the temporal evolution of the disease calibrating our model with real data. Comment: 18 pages, 5 figures.Accepted in Scientific Reports |
| Databáze: | OpenAIRE |
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