- Latin hypercube sampling and infectious diseases how to#
- Latin hypercube sampling and infectious diseases code#
- Latin hypercube sampling and infectious diseases series#
Additionally, this method can be applied to the R o of any other infectious disease to estimate the probability of an epidemic outbreak. and Latin hypercube sampling, 20142017 Yi Lu1, Xiaojun Deng2, Jiahui Chen1, Jianying Wang1, Qin Chen1 and Bing Niu1 Abstract Background: African swine fever (ASF) is a devastating infectious disease of pigs. This uncertainty and sensitivity methodology provides results that can aid investigators in understanding the historical epidemiology of TB by quantifying the effect of the transmission processes involved. These results indicated that five of the nine input parameters, because of their estimation uncertainty, were influential in determining the magnitude of R o. The sensitivity of the magnitude of R o to the uncertainty in estimating values of each of the input parameters was assessed. The R o for the susceptible persons who developed TB slowly (R o Slow) contributed the most to the R o estimates however, the relative R o Slow contribution decreased as the severity of TB epidemics increased. (The three components of R o are associated with fast, slow, and relapse TB.) R o estimates indicated the existence of fairly severe epidemics when TB epidemics first arose. The uncertainty analysis allowed for the derivation of a frequency distribution for R o and the assessment of the relative contribution each of the three components of R o made when TB epidemics first arose centuries ago. On the basis of replicated Latin hypercube sampling, the authors performed an uncertainty and sensitivity analysis of the basic reproductive rate of tuberculosis (TB). To find out more, or to apply click here.The basic reproductive rate (R o) is a measure of the severity of an epidemic. 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.Simulation, sensitivity and sampling parameter sets, including Latin Hypercube sampling.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. Infectious individuals can recover (R) from the disease or they may die from the disease and become. They will learn how to code stochastic and deterministic epidemic models from scratch. Ebola mathematical model Latin hypercube sampling. 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. 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. A short course taught by members of the Centre for the Mathematical Modelling of Infectious Diseases.