dorsal/arxiv
View SchemaRecurrent epidemics in small world networks
| Authors | J. Verdasca, M. M. Telo da Gama, A. Nunes, N. R. Bernardino, J. M. Pacheco, M. C. Gomes |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0408002 |
| URL | https://arxiv.org/abs/q-bio/0408002 |
Abstract
The effect of spatial correlations on the spread of infectious diseases was investigated using a stochastic SIR (Susceptible-Infective-Recovered) model on complex networks. It was found that in addition to the reduction of the effective transmission rate, through the screening of infectives, spatial correlations may have another major effect through the enhancement of stochastic fluctuations. As a consequence large populations will have to become even larger to 'average out' significant differences from the solution of deterministic models. Furthermore, time series of the (unforced) model provide patterns of recurrent epidemics with slightly irregular periods and realistic amplitudes, suggesting that stochastic models together with complex networks of contacts may be sufficient to describe the long term dynamics of some diseases. The spatial effects were analysed quantitatively by modelling measles and pertussis, using a SEIR (Susceptible-Exposed-Infective-Recovered) model. Both the period and the spatial coherence of the epidemic peaks of pertussis are well described by the unforced model for realistic values of the parameters.
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"abstract": "The effect of spatial correlations on the spread of infectious diseases was\ninvestigated using a stochastic SIR (Susceptible-Infective-Recovered) model on\ncomplex networks. It was found that in addition to the reduction of the\neffective transmission rate, through the screening of infectives, spatial\ncorrelations may have another major effect through the enhancement of\nstochastic fluctuations. As a consequence large populations will have to become\neven larger to \u0027average out\u0027 significant differences from the solution of\ndeterministic models. Furthermore, time series of the (unforced) model provide\npatterns of recurrent epidemics with slightly irregular periods and realistic\namplitudes, suggesting that stochastic models together with complex networks of\ncontacts may be sufficient to describe the long term dynamics of some diseases.\nThe spatial effects were analysed quantitatively by modelling measles and\npertussis, using a SEIR (Susceptible-Exposed-Infective-Recovered) model. Both\nthe period and the spatial coherence of the epidemic peaks of pertussis are\nwell described by the unforced model for realistic values of the parameters.",
"arxiv_id": "q-bio/0408002",
"authors": [
"J. Verdasca",
"M. M. Telo da Gama",
"A. Nunes",
"N. R. Bernardino",
"J. M. Pacheco",
"M. C. Gomes"
],
"categories": [
"q-bio.PE"
],
"title": "Recurrent epidemics in small world networks",
"url": "https://arxiv.org/abs/q-bio/0408002"
},
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