The Clustering of Infected SIV Cells in Lymphatic Tissue (original) (raw)
Related papers
2004
The topic of this paper is the distribution of the distance between two points distributed independently in space. We illustrate the use of this interpoint distance distribution to describe the characteristics of a set of points within some ÿxed region. The properties of its sample version, and thus the inference about this function, are discussed both in the discrete and in the continuous setting. We illustrate its use in the detection of spatial clustering by application to a well-known leukaemia data set, and report on the results of a simulation experiment designed to study the power characteristics of the methods within that study region and in an artiÿcial homogenous setting.
HIV with contact tracing: a case study in approximate Bayesian computation
Biostatistics, 2010
Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed for making inference in epidemiological models. It is a likelihood-free method that relies exclusively on numerical simulations. ABC consists in computing a distance between simulated and observed summary statistics and weighting the simulations according to this distance. We propose an original extension of ABC to path-valued summary statistics, corresponding to the cumulated number of detections as a function of time. For a standard compartmental model with Suceptible, Infectious and Recovered individuals (SIR), we show that the posterior distributions obtained with ABC and MCMC are similar. In a refined SIR model well-suited to the HIV contacttracing data in Cuba, we perform a comparison between ABC with full and binned detection times. For the Cuban data, we evaluate the efficiency of the detection system and predict the evolution of the HIV-AIDS disease. In particular, the percentage of undetected infectious individuals is found to be of the order of 40%.
PloS one, 2016
A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models...
Ebola virus (EBV) disease is globally acknowledged public health emergence, which is endemic in the West and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impart of reported violence of the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) that allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation based on the integrated nested Laplace approximation (INLA). Our findings reveal strong association between violent events in the affected areas and the reported EBV cases and deaths, and the presence of clusters for both cases and deaths both of which spread to neighbouring locations in similar manners. Findings from the study are therefo...
Bayesian analysis of a dynamical model for the spread of the Usutu virus
Stochastic Environmental Research and Risk Assessment, 2010
The Usutu virus is an arbovirus transmitted by mosquitoes and causing disease in birds. The virus was detected in Austria for the first time in 2001, while a major outbreak occurred in 2003. developed a nine-compartment deterministic SEIR model to explain the spread of the disease. We extended this to a hierarchical Bayes model assuming random variation in temperature data, in reproduction data of birds, and in the number of birds found to be infected. The model was implemented in R, combined with the FORTRAN subroutine for the original deterministic model. Analysis was made by MCMC using a random walk Metropolis scheme. Posterior means, medians, and credible intervals were calculated for the parameters. The hierarchical Bayes approach proved to be fruitful in extending the deterministic model into a stochastic one. It allowed for Bayesian point and interval estimation and quantification of uncertainty of predictions. The analysis revealed that some model parameters were not identifiable; therefore we kept constant some of them and analyzed others conditional on them. Identifiability problems are common in models aiming to mirror the mechanism of the process, since parameters with natural interpretation are likely to exhibit interrelationships. This study illustrated that Bayesian modeling combined with conditional analysis may help in those cases. Its application to the Usutu model improved model fit and revealed the structure of interdependencies between model parameters: it demonstrated that determining some of them experimentally would enable estimation of the others, except one of them, from available data.
Bayesian epidemic models for spatially aggregated count data
Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein– Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control , thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece.
Spatial Modeling and Mapping of Tuberculosis Using Bayesian Hierarchical Approaches
Open Journal of Statistics, 2016
Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes. The use of frequentist methods in biostatistics and epidemiology is common and is therefore extensively utilized in answering varied research questions. In this paper, we applied the hierarchical Bayesian approach to study the spatial distribution of tuberculosis in Kenya. The focus was to identify best fitting model for modeling TB relative risk in Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages was used for simulations. The Deviance Information Criterion (DIC) proposed by [1] was used for models comparison and selection. Among the models considered, unstructured heterogeneity model perfumes better in terms of modeling and mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB Relative Risk (RR). HIV prevalence is identified as the dominant determinant of TB. We find clustering and heterogeneity of risk among high rate counties. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial methodology for the statistical analysis of risk from TB in Kenya.
A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping
arXiv (Cornell University), 2022
Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions, and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalised smoothing splines to coordinates, we create 2-dimensional smooth surfaces describing the spatial structure of the data whilst making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies we show that the splines can be applied to any continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, whilst allowing for future extensions and multiple sources of spatial connectivity.
Spatial approximations of network-based individual level infectious disease models
Spatial and Spatio-temporal Epidemiology, 2013
Often, when modeling infectious disease spread, the complex network through which the disease propagates is approximated by simplified spatial information. Here, we simulate epidemic spread through various contact networks and fit spatial-based models in a Bayesian framework using Markov chain Monte Carlo methods. These spatial models are individual-level models which account for the spatio-temporal dynamics of infectious disease. The focus here is on choosing a spatial model which best predicts the true probabilities of infection, as well as determining under which conditions such spatial models fail. Spatial models tend to predict infection probability reasonably well when disease spread is propagated through contact networks in which contacts are only within a certain distance of each other. If contacts exist over long distances, the spatial models tend to perform worse when compared to the network model.