39 research outputs found

    Approximate Bayesian Computation for infectious disease modelling.

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    Approximate Bayesian Computation (ABC) techniques are a suite of model fitting methods which can be implemented without a using likelihood function. In order to use ABC in a time-efficient manner users must make several design decisions including how to code the ABC algorithm and the type of ABC algorithm to use. Furthermore, ABC relies on a number of user defined choices which can greatly effect the accuracy of estimation. Having a clear understanding of these factors in reducing computation time and improving accuracy allows users to make more informed decisions when planning analyses. In this paper, we present an introduction to ABC with a focus of application to infectious disease models. We present a tutorial on coding practice for ABC in R and three case studies to illustrate the application of ABC to infectious disease models

    Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa.

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    Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.MBG

    Modelling Sand Fly Lutzomyia longipalpis Attraction to Host Odour: Synthetic Sex-Aggregation Pheromone Dominates the Response.

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    Zoontic visceral leishmaniasis (ZVL) due to Leishmania infantum is a potentially fatal protozoan parasitic disease of humans and dogs. In the Americas, dogs are the reservoir and the sand fly, Lutzomyia longipalpis, the principal vector. A synthetic version of the male sand fly produced sex-aggregation pheromone attracts both female and male conspecifics to co-located insecticide, reducing both reservoir infection and vector abundance. However the effect of the synthetic pheromone on the vector's "choice" of host (human, animal reservoir, or dead-end host) for blood feeding in the presence of the pheromone is less well understood. In this study, we developed a modelling framework to allow us to predict the relative attractiveness of the synthetic pheromone and potential alterations in host choice. Our analysis indicates that the synthetic pheromone can attract 53% (95% CIs: 39%-86%) of host-seeking female Lu. longipalpis and thus it out-competes competing host odours. Importantly, the results suggest that the synthetic pheromone can lure vectors away from humans and dogs, such that when co-located with insecticide, it provides protection against transmission leading to human and canine ZVL

    Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems

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    Background and Aims: Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high resolution method is presented that can be applied to single and multi-species cropping systems, to facilitate characterisation of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut (Vigna subterranea (L.) Verdc.) and Proso millet (Panicum miliaceum) have been used as an example and the new opportunities presented by this approach have been analysed. Methods: Three-dimensional plant reconstruction, based on stereocameras, combined with ray-tracing was implemented to explore the light environment within the Bambara groundnut-Proso millet intercropping system and associated monocrops. Gas exchange data was used to predict the total carbon gain of each component crop. Key Results: The shading influence of the tall Proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of Proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area. Conclusions: Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multispecies cropping systems; enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops or; enables the prediction of new productive combinations of previously untested crops

    A global profile of replicative polymerase usage

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    Three eukaryotic DNA polymerases are essential for genome replication. Polymerase (Pol) α–primase initiates each synthesis event and is rapidly replaced by processive DNA polymerases: Polɛ replicates the leading strand, whereas Polδ performs lagging-strand synthesis. However, it is not known whether this division of labor is maintained across the whole genome or how uniform it is within single replicons. Using Schizosaccharomyces pombe, we have developed a polymerase usage sequencing (Pu-seq) strategy to map polymerase usage genome wide. Pu-seq provides direct replication-origin location and efficiency data and indirect estimates of replication timing. We confirm that the division of labor is broadly maintained across an entire genome. However, our data suggest a subtle variability in the usage of the two polymerases within individual replicons. We propose that this results from occasional leading-strand initiation by Polδ followed by exchange for Polɛ

    Dynamics of DNA replication in yeast

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    We present a mathematical model for the spatial dynamics of DNA replication. Using this model we determine the probability distribution for the time at which each chromosomal position is replicated. From this we show, contrary to previous reports, that mean replication time curves cannot be used to directly determine origin parameters. We demonstrate that the stochastic nature of replication dynamics leaves a clear signature in experimentally measured population average data, and we show that the width of the activation time probability distribution can be inferred from this data. Our results compare favorably with experimental measurements in Saccharomyces cerevisae
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