8,484 research outputs found
On the Alcestis and Andromache of Euripides
published or submitted for publicatio
Enhancing Bayesian risk prediction for epidemics using contact tracing
Contact tracing data collected from disease outbreaks has received relatively
little attention in the epidemic modelling literature because it is thought to
be unreliable: infection sources might be wrongly attributed, or data might be
missing due to resource contraints in the questionnaire exercise. Nevertheless,
these data might provide a rich source of information on disease transmission
rate. This paper presents novel methodology for combining contact tracing data
with rate-based contact network data to improve posterior precision, and
therefore predictive accuracy. We present an advancement in Bayesian inference
for epidemics that assimilates these data, and is robust to partial contact
tracing. Using a simulation study based on the British poultry industry, we
show how the presence of contact tracing data improves posterior predictive
accuracy, and can directly inform a more effective control strategy.Comment: 40 pages, 9 figures. Submitted to Biostatistic
Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
In low-resource settings, prevalence mapping relies on empirical prevalence
data from a finite, often spatially sparse, set of surveys of communities
within the region of interest, possibly supplemented by remotely sensed images
that can act as proxies for environmental risk factors. A standard
geostatistical model for data of this kind is a generalized linear mixed model
with binomial error distribution, logistic link and a combination of
explanatory variables and a Gaussian spatial stochastic process in the linear
predictor. In this paper, we first review statistical methods and software
associated with this standard model, then consider several methodological
extensions whose development has been motivated by the requirements of specific
applications. These include: methods for combining randomised survey data with
data from non-randomised, and therefore potentially biased, surveys;
spatio-temporal extensions; spatially structured zero-inflation. Throughout, we
illustrate the methods with disease mapping applications that have arisen
through our involvement with a range of African public health programmes.Comment: Submitte
On The Inverse Geostatistical Problem of Inference on Missing Locations
The standard geostatistical problem is to predict the values of a spatially
continuous phenomenon, say, at locations using data
where is the realization at location of
, or of a random variable that is stochastically related to
. In this paper we address the inverse problem of predicting the
locations of observed measurements . We discuss how knowledge of the
sampling mechanism can and should inform a prior specification, say,
for the joint distribution of the measurement locations , and propose an efficient Metropolis-Hastings algorithm for
drawing samples from the resulting predictive distribution of the missing
elements of . An important feature in many applied settings is that this
predictive distribution is multi-modal, which severely limits the usefulness of
simple summary measures such as the mean or median. We present two simulated
examples to demonstrate the importance of the specification for , and
analyze rainfall data from Paran\'a State, Brazil to show how, under additional
assumptions, an empirical of estimate of can be used when no prior
information on the sampling design is available.Comment: Under revie
Suppression of zinc dendrites in zinc electrode power cells
Addition of various tetraalkyl quarternary ammonium salts, to alkaline zincate electrolyte of cell, prevents formation of zinc dendrites during charging of zinc electrode. Electrode capacity is not impaired and elimination of dendrites prolongs cell life
INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes
We investigate two options for performing Bayesian inference on spatial
log-Gaussian Cox processes assuming a spatially continuous latent field: Markov
chain Monte Carlo (MCMC) and the integrated nested Laplace approximation
(INLA). We first describe the device of approximating a spatially continuous
Gaussian field by a Gaussian Markov random field on a discrete lattice, and
present a simulation study showing that, with careful choice of parameter
values, small neighbourhood sizes can give excellent approximations. We then
introduce the spatial log-Gaussian Cox process and describe MCMC and INLA
methods for spatial prediction within this model class. We report the results
of a simulation study in which we compare MALA and the technique of
approximating the continuous latent field by a discrete one, followed by
approximate Bayesian inference via INLA over a selection of 18 simulated
scenarios. The results question the notion that the latter technique is both
significantly faster and more robust than MCMC in this setting; 100,000
iterations of the MALA algorithm running in 20 minutes on a desktop PC
delivered greater predictive accuracy than the default \verb=INLA= strategy,
which ran in 4 minutes and gave comparative performance to the full Laplace
approximation which ran in 39 minutes.Comment: This replaces the previous version of the report. The new version
includes results from an additional simulation study, and corrects an error
in the implementation of the INLA-based method
A space-time conditional intensity model for infectious disease occurence
A novel point process model continuous in space-time is proposed for infectious disease data. Modelling is based on the conditional intensity function (CIF) and extends an additive-multiplicative CIF model previously proposed for discrete space epidemic modelling. Estimation is performed by means of full maximum likelihood and a simulation algorithm is presented. The particular application of interest is the stochastic modelling of the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002–2008. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modelling, simulation and inference of self-exciting spatio-temporal point processes based on the CIF. Application is promoted by an implementation in the R package RLadyBug
On the effect of preferential sampling in spatial prediction
The choice of the sampling locations in a spatial network is often guided by practical demands. In particular, many locations are preferentially chosen to capture high values of a response, for example, air pollution levels in environmental monitoring. Then, model estimation and prediction of the exposure surface become biased due to the selective sampling. Since prediction is often the main utility of the modeling, we suggest that the effect of preferential sampling lies more importantly in the resulting predictive surface than in parameter estimation. Our contribution is to offer a direct simulation-based approach to assessing the effects of preferential sampling. We compare two predictive surfaces over the study region, one originating from the notion of an ‘operating’ intensity driving the selection of monitoring sites, the other under complete spatial randomness. We can consider a range of response models. They may reflect the operating intensity, introduce alternative informative covariates, or just propose a flexible spatial model. Then, we can generate data under the given model. Upon fitting the model and interpolating (kriging), we will obtain two predictive surfaces to compare. It is important to note that we need suitable metrics to compare the surfaces and that the predictive surfaces are random, so we need to make expected comparisons
- …