Phase lag in epidemics on a network of cities

Rozhnova, Ganna; Nunes, Ana; McKane, Alan J.

doi:10.1103/physreve.85.051912

Cited by 16 publications

(28 citation statements)

References 28 publications

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“…When analysing a large geographical area made of several interacting communities, synchronization and persistence have been often considered in association because asynchrony was considered a possible reinfection mechanism between neighbouring areas after local fadeouts. Several works have shown that synchronization between spatially distributed communities depends on the strength of their coupling as well as the intensity of the external seasonality [3][4][5][6][7][8][9]. Ruxton [3] explored the synchronization and persistence in weakly coupled non-seasonal chaotic systems and found that they did not fully synchronize, thus permitting re-colonization of local patches after extinction occurred.…”

Section: Introductionmentioning

confidence: 99%

Periodicity, synchronization and persistence in pre-vaccination measles

Marguta

Parisi

2016

J. R. Soc. Interface.

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We investigate the relationship between periodicity, synchronization and persistence of measles through simulations of geographical spread on the British Isles. We show that the establishment of areas of biennial periodicity depends on the interplay between human mobility and local population size and that locations undergoing biennial cycles tend to be, on average, synchronized in phase. We show however that occurrences of opposition of phase are actually quite common and correspond to stable dynamics. We also show that persistence is strictly related to circulation of the disease in the highly populated area of London and that this ensures survival of the disease even when human mobility drops to extremely low levels.

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Section: Introductionmentioning

confidence: 99%

Periodicity, synchronization and persistence in pre-vaccination measles

Marguta

Parisi

2016

J. R. Soc. Interface.

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“…Our model is clearly related to meta-population models, that have been considered previously as models of infection spread in aggregated populations (Lloyd and May, 1996;Riley and others, 2003;Rozhnova et al, 2012). The novelty in our work is that we consider a different population level limit -metapopulations often are considered as a small number of large clumps (for example representing cities in a country), where as we consider a large number of small clumps (more reminiscent of households within a country).…”

Section: Discussionmentioning

confidence: 99%

The effect of clumped population structure on the variability of spreading dynamics

Black

House

Keeling

et al. 2014

Journal of Theoretical Biology

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Processes that spread through local contact, including outbreaks of infectious diseases, are inherently noisy, and are frequently observed to be far noisier than predicted by standard stochastic models that assume homogeneous mixing. One way to reproduce the observed levels of noise is to introduce significant individual-level heterogeneity with respect to infection processes, such that some individuals are expected to generate more secondary cases than others. Here we consider a population where individuals can be naturally aggregated into clumps (subpopulations) with stronger interaction within clumps than between them. This clumped structure induces significant increases in the noisiness of a spreading process, such as the transmission of infection, despite complete homogeneity at the individual level. Given the ubiquity of such clumped aggregations (such as homes, schools and workplaces for humans or farms for livestock) we suggest this as a plausible explanation for noisiness of many epidemic time series.

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“…The general procedure to do this is reviewed in Ref. [27] and has been previously applied to models of epidemics on networks [28,29], albeit in situations where the nodes of the network contained many individuals, rather than just one, as in the present context.…”

Section: Formulation Of the Modelmentioning

confidence: 99%

Stochastic epidemic dynamics on extremely heterogeneous networks

2016

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Networks of contacts capable of spreading infectious diseases are often observed to be highly heterogeneous, with the majority of individuals having fewer contacts than the mean, and a significant minority having relatively very many contacts. We derive a two-dimensional diffusion model for the full temporal behavior of the stochastic susceptible-infectious-recovered (SIR) model on such a network, by making use of a time-scale separation in the deterministic limit of the dynamics. This low-dimensional process is an accurate approximation to the full model in the limit of large populations, even for cases when the time-scale separation is not too pronounced, provided the maximum degree is not of the order of the population size.

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Phase lag in epidemics on a network of cities

Cited by 16 publications

References 28 publications

Periodicity, synchronization and persistence in pre-vaccination measles

Periodicity, synchronization and persistence in pre-vaccination measles

The effect of clumped population structure on the variability of spreading dynamics

Stochastic epidemic dynamics on extremely heterogeneous networks

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