2,285 research outputs found
Modified individual-level models of infectious disease
No full textInfectious disease models can be used to understand mechanisms of the spread of diseases and thus, may effectively guide control policies for potential outbreaks. Deardon et al. (2010) introduced a class of individual-level models (ILMs) which are highly flexible. Parameter estimates for ILMs can be achieved by means of Markov chain Monte Carlo (MCMC) methods within a Bayesian framework. Here, we introduce an extended form of ILM, described by Deardon et al. (2010), and compare this model with the original ILM in the context of a simple spatial system. The two spatial ILMs are fitted to 70 simulated data sets and a real data set on tomato spotted wilt virus (TSWV) in pepper plants (Hughes et al., 1997). We find that the modified ILM is more flexible than the original ILM and may fit some data sets better
Modeling Heterogeneity in Infectious Disease Systems for Inference and Monitoring
No full textNonhomogeneity in infectious disease spread can be described most directly via a population that is heterogeneous at the individual level. Spatial and network-based individual level models (ILMs) of Deardon et al. (2010) are two classes of models that describe such a population, and that have been successfully applied to human, animal, and plant diseases. ILMs allow the use of covariate information at the individual level (e.g. spatial location, number of contacts, etc.); the cost for this level of detail, however, is the computational time they take to be fitted to data. This thesis considers of a selection of topics on inference and surveillance for such models. One general theme is the reduction in computational burden associated with IBMs via aggregation and mathematical approximations. First, we consider a spatial ILM adapted to a system with two competing pathogens. A data-intensive model is first proposed for inference within a Bayesian MCMC framework, and then approximated by a faster model that utilizes aggregated data. The second topic develops an inference methodology for a network model that has a given degree distribution. Following results from Volz (2008) and Miller (2011), we develop an analytic likelihood for count data, and fit this to single and multi-season epidemics. Thirdly, we employ the same network model to test various surveillance systems. Using simulation, we derive distributional results for alarms meant to determine the start of seasonal epidemics, and compare their performance. All of our methods are tested on simulated data, and in addition, we use real influenza data sets to illustrate the methods related to network models.Ontario Ministry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationCentre for Public Health and Zoonose
Phylodynamic and Transmission Network Individual Level Infectious Disease Models
No full textThe individual level model (ILM) framework of Deardon et al. (2010) outlines the incorporation of individual specific risk factors into infectious disease models. ILMs represent individual-specific disease state transitions, and allow for investigation of hypotheses regarding overall risk to individuals. Such investigations are relevant in the development of projections and control policies while considering population heterogeneity. We extend the ILM framework to allow for competing risks of disease transmission with Transmission Network ILMs (TN-ILMs). The data requirements of TN-ILMs includes the typically latent transmission times, and transmission network, so we present TN-ILMs along Bayesian data augmentation methods to infer TN-ILM parameters jointly with these latent data. Our Markov Chain Monte Carlo based inference strategy for TN-ILMs is implemented in Pathogen.jl, a high performance, and highly flexible statistical software package in the Julia language. Pathogen.jl supports simulation, inference, and visualization of epidemics from Susceptible-Infected (SI), Susceptible-Exposed-Infected (SEI), Susceptible-Infected-Removed (SIR), and Susceptible-Exposed-Infected-Removed (SEIR) TN-ILMs. Applications of TN-ILMs using Pathogen.jl are presented for the 1861 Hagelloch measles outbreak (Pfeilsticker, 1863; Oesterle, 1992) and an experimental tomato spotted wilt virus outbreak (Hughes et al. 1997). We further extend TN-ILMs to full phylodynamic ILMs. Phylodynamics is the combined study of disease spread and evolution. Phylodynamic approaches are most appropriate when dense genetic sampling has been conducted on the pathogen during an outbreak, and evolutionary and epidemiological processes occur on a similar time scale. With the phylodynamic ILM extension, we can jointly infer disease transmission times, transmission network, pathogen phylogeny, and the phylodynamic ILM parameters. We contrast a fully phylodynamic approach to one that incorporates genetic distances as a dyadic covariate in various TN-ILMs, and show that phylodynamic ILMs offer improved event time and transmission network inference, at a significantly increased computational cost.Ontario Ministry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of Canad
On the Effect of Ignoring Within-Unit Infectious Disease Dynamics When Modelling Spatial Transmission
No full textIndividual-level models (ILMs) are a class of models that can be used to analyze infectious epidemic data to assist in the understanding of the spatio-temporal dynamics of infectious diseases in discrete time (Deardon et al., 2010). ILMs are generally fitted to epidemic data through Markov chain Monte Carlo (MCMC) methods in a Bayesian statistical framework. Here, we test the effect of ignoring within-unit (e.g., city) infectious disease dynamics when we model spatial transmission. We do this by generating our epidemic data sets from a true model which considers within unit dynamics. It is often hard to get individual-level data in reality. Also, the R package EpiILM used in this thesis for model fitting does not allow for within unit dynamics. For these reasons, we cannot easily fit our generating model to data. We fitted two ILM models (one model with a covariate representing city size, and the other model without covariates), in which within unit dynamics are not explicitly accounted for. We have found from our analysis that the model with the covariate may be a slightly better model to describe the spatio-temporal dynamics of the epidemic. However, although the model with the covariate is better in describing the epidemic process, the dynamics are still not perfectly captured by this model. Our results show the dangers inherent in ignoring within unit dynamics when modelling spatial disease transmission
Sampling-Based Likelihood Approximations for Infectious Disease Models and Other Related Topics
No full textDeardon et al. (2010) describe a class of individual-level models (ILMs), fitted in a Bayesian framework using Markov chain Monte Carlo (MCMC) techniques. They are used to model the spread of infectious diseases in discrete time. A key feature of these ILMs is that they take into account covariate information on susceptible and infectious individuals as well as shared covariate information such as geography or contact measures. These models quantify probabilistic outcomes regarding the risk of infection. ILMs are developed and fitted to data sets from two studies on influenza transmission within households in Hong Kong during 2008--2009 and 2009--2010. The goal is to estimate the effect of vaccination on infection risk and choose a model that best fits the infection data. The infectious pressure exerted on susceptible individuals defines the hazard rate (in survival analysis terminology) for individuals. Unfortunately, quantifying this infectious pressure for each individual over time can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, MCMC-based analysis. Therefore, we introduce sampling methods to speed-up the calculation of the likelihood function. We compare a simple random sampling scheme with a number of spatially-stratified sampling approaches. The performances of the sampling-based likelihood approximations are tested and compared via simulation studies, and using data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Data augmentation is a technique used in Bayesian inference that allows the parameter set to be augmented by parameters representing missing or censored data. Here, infection times are treated as missing information. The problem of computation time worsens when using data augmentation to allow for uncertainty in infection times due to a significant increase in the number of times the likelihood function is calculated at each MCMC step. Therefore, we expand the data-sampling-based likelihood approximating algorithms and develop sampling methods that allow for data augmented infection times parameters. Once again, a simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performances of our methods using simulated data, and data from the 2001 FMD epidemic in the U.K.Ontario Ministry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovatio
Individual-Level Modelling of Infectious Disease Data: EpiILM
No full textIn this article we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. Use of the package is demonstrated through examples involving both simulated and real data
Adventures of a currency trader : a fable about trading, courage, and doing the right thing / Rob Booker.
No full textIncludes index.Book fair 2012.xv, 221 pages :Praise for ADVENTURES of a CURRENCY TRADER "A truly easy, unique, and enjoyable read! Rob has done it onceagain to teach us in the funniest way possible...
how not to make themost common trading mistakes. If you are tired of reading how-tobooks, this is perfect for you. I highly recommend this book to alltraders. Everyone will learn something about themselves by readingthis book."—Kathy Lien, author, Day Trading the Currency Market,and Chief Strategist, www.dailyfx.com"Adventures of a Currency Trader is a must read foranyone who has ever traded or is thinking about trading in theForex markets. Rob Booker has a unique way of taking years ofmarket knowledge and transforming it into an educational andentertaining experience. It has quickly become a cult classic in mytrading library!"—H. Jack Bouroudjian, Principal, Brewer Investment Group"Brilliant! Rob's humor and humanity shine through in thisparable about trading and life. Filled with wisdom and wit, it's anexhilarating rollercoaster ride through the peaks and valleys ofthe learning curve, with many valuable lessons learned along theway."—Ed Ponsi, President, FXEducator.com"Rob's fable of everyman 'Harry Banes' is destined to become atrading classic. This is both the missing piece and the foundationthat comes before the strategies and methodologies. The search forthe Holy Grail begins and ends in the heart and mind. The journeyis authentic and real and if you're willing to take it with Rob,you will be rewarded in the end. Seldom has psychology and wisdombeen so entertaining!"—Raghee Horner, trader and author of Forex Trading forMaximum Profit and Days of Forex Trading"In a series of insightful and entertaining vignettes, RobBooker teaches both the novice and the experienced trader some hardwon truths about the currency market. It's a must read book writtenby a guy who survived the trenches and went on to prosper in thebiggest and most competitive financial market in the world."—Boris Schlossberg, Senior Currency Strategist, Forex CapitalMarkets LLC, and author of Technical Analysis of the CurrencyMarke
Parameter Estimation in Individual-Level Models of Infectious Disease
No full textSpatial epidemic models are crucial to the prediction and control of infectious disease spread. Although the effect of a spatial parameter (β) on disease transmission may be more apparent in dense populations, knowledge of the spatial component of epidemic transmission is used to inform vaccination policies and culling procedures in many settings. Additionally, the susceptibility (α) of the population at risk and the infectious period (γ) affect the speed of epidemic spread. We compare the parameter estimation techniques of maximum likelihood estimation and the gold standard Bayesian MCMC in terms of width of confidence and credible intervals for all three model parameters in two epidemic frameworks. We examine the effect of misspecification of the infectious period γ on estimation of α and β. A grid population and a population generated by bivariate normal distributions are considered. We find that epidemics travel more quickly over the highly dense population regardless of the value of γ.Ontario Ministry of Agriculture, Food and Rural Affair
Back-calculation, Classification, and Emulation-based Inference for Spatial Infectious Disease Models
No full textIndividual-level models (ILMs) are a class of complex probabilistic models which can be used to model infectious disease data. They can incorporate the effects of time and space, the key risk factors of disease transmission, and inference for them is carried out easily within a Bayesian MCMC framework. They are thus useful for modelling individual-level spatial epidemic data. However, fitting these models to what are typically incomplete data can result in poor parameter estimation and can miss important characteristics of the disease systems. Additionally, the complex nature of such ILMs can cause significant computational expense when fitting them to large disease systems. Here, we propose methods and models which address both the incomplete history of epidemic data as well as the computational problem of inference for ILMs while modelling complex disease systems. First, we consider the use of back-calculation of infection times in the context of spatial infectious disease models. Together with prior knowledge about the distribution of the time from infection to disease reporting, we extend the method to incorporate spatial information in the back-calculation mechanism itself. Secondly, the epidemic classification approach of Nsoesie et al.(2011) is extended to the case where the disease generating models are spatial ILMs. This method involves simulating epidemics from various spatial ILMs, and then using a classifier built from the epidemic curve data to predict which model was most likely to have generated an observed epidemic curve. Finally, we propose a method of inference for spatial ILMs based on so-called emulation techniques. The method is set in a Bayesian MCMC context, but avoids calculation of the computationally expensive likelihood function by replacing it with a Gaussian process approximation of the likelihood function of the ILM built from simulated data. All models are fitted to simulated as well as real data, specifically data from an experiment on tomato spotted wilt virus (TSWV)
Time-varying Individual-level Infectious Disease Models
No full textIndividual-level models (ILMs) of infectious disease spread are a system of statistical models which can be used to model infectious disease transmission through a population in discrete-time. These models allow researchers to incorporate risk factors at the individual level; thus they are suited for modeling epidemics spatially. Individuals, here, may refer to people, animals, or plants, or aggregated units such as animals on a farm or students in a school. ILMs are usually fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo (MCMC) methods. Ideally, covariate data and the infection status of individuals over time would be used to obtain parameter estimates for the ILMs. However, owing to various practical reasons, there are often situations in which the collection of infectious disease data at the individual level is infeasible. Instead, infectious disease data is collected at a regional level (e.g. a level which actually consists of spatially aggregated sets of individual units), such as health units or census regions. Therefore, it is reasonable to assume that the infectivity of such aggregated units varies as the status of infectiousness (i.e. the number/proportion of infectious individuals) within the aggregated unit changes. In the thesis, ILMs are extended to allow for time-varying susceptibility, infectivity and contact functions. A series of time-varying infectivity ILMs (TVI-ILMs) are then developed for the problem of modeling disease spread at the regional level. A method of carrying out model comparison and assessment based on the use of probability scoring rules is also developed and explored. Finally, the TVI-ILMs are extended to allow for infectivity curves that are dependent on regional-level covariates. Models and methods are tested on a combination of simulated epidemic data, and data from the 2009 H1N1 influenza pandemic collected in Southern Ontario.Natural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of GuelphCanada Foundation for InnovationGEOIDE Networ
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