Dynamic vaccination in partially overlapped multiplex network
| Autor: | Lidia A. Braunstein, M. A. Di Muro, Shlomo Havlin, Lucila G. Alvarez-Zuzek |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Physics - Physics and Society
GENERATING FUNCTION FRAMEWORK Steady state (electronics) Ciencias Físicas FOS: Physical sciences Physics and Society (physics.soc-ph) Topology Otras Ciencias Físicas BOND PERCOLATION 01 natural sciences Quantitative Biology::Other 010305 fluids & plasmas purl.org/becyt/ford/1 [https] Networks and Complex Systems 0103 physical sciences Quantitative Biology::Populations and Evolution Fraction (mathematics) Multiplex 010306 general physics Mathematics SPREADING DISEASES MODEL Percolation (cognitive psychology) Diagram Articles purl.org/becyt/ford/1.3 [https] Immunization (finance) Vaccination MULTIPLEX NETWORKS Epidemic model CIENCIAS NATURALES Y EXACTAS |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET Physical Review. E |
| Popis: | In this work we propose and investigate a new strategy of vaccination, which we call "dynamic vaccination". In our model, susceptible people become aware that one or more of their contacts are infected, and thereby get vaccinated with probability $\omega$, before having physical contact with any infected patient. Then, the non-vaccinated individuals will be infected with probability $\beta$. We apply the strategy to the SIR epidemic model in a multiplex network composed by two networks, where a fraction $q$ of the nodes acts in both networks. We map this model of dynamic vaccination into bond percolation model, and use the generating functions framework to predict theoretically the behavior of the relevant magnitudes of the system at the steady state. We find a perfect agreement between the solutions of the theoretical equations and the results of stochastic simulations. In addition, we find an interesting phase diagram in the plane $\beta-\omega$, which is composed by an epidemic and a non-epidemic phases, separated by a critical threshold line $\beta_c$, which depends on $q$. Wefind that, for all values of $q$, a region in the diagram where the vaccination is so efficient that, regardless of the virulence of the disease, it never becomes an epidemic. We compare our strategy with random immunization and find that using the same amount of vaccines for both scenarios, we obtain that the spread of the disease is much lower in the case of dynamic vaccination when compared to random immunization. Furthermore, we also compare our strategy with targeted immunization and we find that, depending on $\omega$, dynamic vaccination will perform significantly better, and in some cases will stop the disease before it becomes an epidemic. Comment: 25 pages, 6 figures |
| Databáze: | OpenAIRE |
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