Asymmetry in the Presence of Migration Stabilizes Multistrain Disease Outbreaks
Identifieur interne : 000B26 ( Istex/Checkpoint ); précédent : 000B25; suivant : 000B27Asymmetry in the Presence of Migration Stabilizes Multistrain Disease Outbreaks
Auteurs : Simone Bianco [États-Unis] ; Leah B. Shaw [États-Unis]Source :
- Bulletin of Mathematical Biology [ 0092-8240 ] ; 2011-01-01.
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Abstract
Abstract: We study the effect of migration between coupled populations, or patches, on the stability properties of multistrain disease dynamics. The epidemic model used in this work displays a Hopf bifurcation to oscillations in a single, well-mixed population. It is shown numerically that migration between two non-identical patches stabilizes the endemic steady state, delaying the onset of large amplitude outbreaks and reducing the total number of infections. This result is motivated by analyzing generic Hopf bifurcations with different frequencies and with diffusive coupling between them. Stabilization of the steady state is again seen, indicating that our observation in the full multistrain model is based on qualitative characteristics of the dynamics rather than on details of the disease model.
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DOI: 10.1007/s11538-010-9541-4
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<front><div type="abstract" xml:lang="en">Abstract: We study the effect of migration between coupled populations, or patches, on the stability properties of multistrain disease dynamics. The epidemic model used in this work displays a Hopf bifurcation to oscillations in a single, well-mixed population. It is shown numerically that migration between two non-identical patches stabilizes the endemic steady state, delaying the onset of large amplitude outbreaks and reducing the total number of infections. This result is motivated by analyzing generic Hopf bifurcations with different frequencies and with diffusive coupling between them. Stabilization of the steady state is again seen, indicating that our observation in the full multistrain model is based on qualitative characteristics of the dynamics rather than on details of the disease model.</div>
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