(1/105) Utility of R0 as a predictor of disease invasion in structured populations.

Early theoretical work on disease invasion typically assumed large and well-mixed host populations. Many human and wildlife systems, however, have small groups with limited movement among groups. In these situations, the basic reproductive number, R0, is likely to be a poor predictor of a disease pandemic because it typically does not account for group structure and movement of individuals among groups. We extend recent work by combining the movement of hosts, transmission within groups, recovery from infection and the recruitment of new susceptibles into a stochastic model of disease in a host metapopulation. We focus on how recruitment of susceptibles affects disease invasion and how population structure can affect the frequency of superspreading events (SSEs). We show that the frequency of SSEs may decrease with the reduced movement and the group sizes due to the limited number of susceptible individuals available. Classification tree analysis of the model results illustrates the hierarchical nature of disease invasion in host metapopulations. First, the pathogen must effectively transmit within a group (R0>1), and then the pathogen must persist within a group long enough to allow for movement among the groups. Therefore, the factors affecting disease persistence--such as infectious period, group size and recruitment of new susceptibles--are as important as the local transmission rates in predicting the spread of pathogens across a metapopulation.  (+info)

(2/105) Analyses of the 1957 (Asian) influenza pandemic in the United Kingdom and the impact of school closures.

Many countries plan to close schools during a future influenza pandemic, although the potential impact is poorly understood. We apply a model of the transmission dynamics of pandemic influenza to consultation, serological and clinical data from the United Kingdom from the 1957 (Asian) influenza pandemic, to estimate the basic reproduction number (R0), the proportion of infected individuals who experience clinical symptoms and the impact of school/nursery closures. The R0 for Asian influenza was about 1.8 and 60-65% of infected individuals were estimated to have experienced clinical symptoms. During a future pandemic, closure of schools/nurseries could reduce the epidemic size only by a very small amount (<10%) if R0 is high (e.g. 2.5 or 3.5), and modest reductions, e.g. 22% might be possible if it is low (1.8) and schools are closed early, depending on assumptions about contact patterns. Further data on contact patterns and their dependence on school closures are needed.  (+info)

(3/105) A mathematical model of the dynamics of Salmonella Cerro infection in a US dairy herd.

We developed a mathematical model of the transmission dynamics of salmonella to describe an outbreak of S. Cerro infection that occurred in a Pennsylvania dairy herd. The data were collected as part of a cooperative research project between the Regional Dairy Quality Management Alliance and the Agricultural Research Service. After the initial detection of a high prevalence of S. Cerro infection in the herd, a frequent and intensive sampling was conducted and the outbreak was followed for 1 year. The data showed a persistent presence of S. Cerro with a high prevalence of infection in the herd. The dynamics of host and pathogen were modelled using a set of nonlinear differential equations. A more realistically distributed (gamma-distributed) infectious period using multiple stages of infection was considered. The basic reproduction number was calculated and relevance to the intervention strategies is discussed.  (+info)

(4/105) The effect of heterogeneous infectious period and contagiousness on the dynamics of Salmonella transmission in dairy cattle.

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(5/105) Epidemic thresholds in dynamic contact networks.

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(6/105) Household structure and infectious disease transmission.

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(7/105) Latent coinfection and the maintenance of strain diversity.

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(8/105) Imported and autochthonous cases in the dynamics of dengue epidemics in Brazil.

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