Latin hypercube sampling and infectious diseases
- LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES HOW TO
- LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES CODE
- LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES SERIES
To find out more, or to apply click here. The course is ideal for those who will be conducting research using infectious disease models in R or who want a deeper understanding of techniques for implementing models.
LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES CODE
Individuals who know some R but do not have experience using R to code infectious disease models will benefit. This course is aimed at people who have had some exposure to the theory and use of infectious disease modelling and who would like to start coding their own models using R. We will provide some exercises before the course to help participants decide if they need to attend the introductory session. A 2-hour Introductory session is available for those with no prior experience with R.
LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES SERIES
The course is taught as a series of hands-on computer practicals in R. Version control: a hands-on introduction to Git and Github.Network models: reading adjacency matrices and simulation of Reed-Frost models.Processing outputs using ggplot2: making graphs and stratifying outputs.Moreover, an optimal control problem is formulated and the necessary optimality conditions are investigated in order to eradicate the disease in a community. Simulation, sensitivity and sampling parameter sets, including Latin Hypercube sampling Further, the global sensitivity analysis of the model is carried out using the Latin Hypercube sampling and the partial rank correlation coefficient techniques.
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Ordinary differential equation models, including using deSolve for integration.Using loops, functions, packages and sourcing in R.Introduction to R (optional morning session).
LATIN HYPERCUBE SAMPLING AND INFECTIOUS DISEASES HOW TO
They will also learn how to present model output by implementing sensitivity analysis and graphing data, and best practices for writing coherent code and using version control. They will learn how to code stochastic and deterministic epidemic models from scratch. They will learn key principles for best practice in model coding including version control. Participants will gain a working knowledge of using R to code dynamic transmission models. With this short course we aim to bridge the gap between theoretical training in infectious disease modelling, and the specialist technical skills needed for research in this area.
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Mathematical models are increasingly used to understand the transmission of infectious diseases in populations and to evaluate the potential impact of control programmes in reducing morbidity and mortality. scapularis than mice.A short course taught by members of the Centre for the Mathematical Modelling of Infectious Diseases. Finally, a global sensitivity analysis based on Latin hypercube sampling is performed, in which is shown the importance of quantifying the natural history of infection in mice, and of elucidating the contribution of other hosts for I. Vertical transmission has a disproportionately large effect, since unfed infected larval ticks have two opportunities to feed on mice, rather than only one opportunity (as for a newly infected unfed nymph). Equilibrium disease levels were examined under the assumption of a constant tick population these levels were determined as a function of tick and mouse density, the vertical transmission rate, the infectivity of mice, and the survivorship parameters of the ticks and of the tick-host contact rates. These expressions show that the transmission chain in which ticks acquire the disease from mice in the fall and transmit it back to mice as nymphs in the spring is the most important chain (contributing approximately 87% of the elasticity of the threshold for the parameter choices examined). Using this model, the threshold condition for the disease to be able to invade a nonenzootic region is determined as a function of the various possible transmission chains operating throughout the year. This model is based on the life history of the vector tick Ixodes scapularis Say and the primary reservoir host Peromyscus leucopus. A mathematical model of enzootic Lyme-disease transmission in a natural focus is presented.