Benjamin Taylor

Lancaster University | LU · Lancaster Medical School

Improvements to Zambia's malaria surveillance system allow better monitoring of incidence and targetting of responses at refined spatial scales. As transmission decreases, understanding heterogeneity in risk at fine spatial scales becomes increasingly important. However, there are challenges in using health system data for high-resolution risk mapp...

Background: In Malaysia, colorectal cancer is the second most common type of cancer for both sexes, represents 10.2% of total cancer cases in Malaysia. This study aims to identify the effect of individual-level factors on survival prognosis for patients with colorectal cancer in Malaysia.Methods: The study involved 4412 of colorectal cancer patient...

Background: The HIV epidemic is a major public health concern throughout Africa. Malawi is one of the worst affected countries in sub-Saharan Africa with a 2014 national HIV prevalence currently estimated at 10% (9.3-10.8%) by UNAIDS. Study reports, largely in the African setting comparing outcomes in HIV patients with and without Kaposi's sarcoma...

This article introduces new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology due to confidentiality issues: it is much more common to know the county in which an individual resides, say, than know their exact location in space. Inference for aggregated data has traditionally m...

Background: Spatio-temporal variation in under-five-year-old children malnutrition remains unstudied in most developing countries like Ghana. This study explores and forecasts the spatio-temporal patterns in childhood chronic malnutrition among these children. We also investigate the effect of maternal education on childhood malnutrition. Methods:...

Survival methods are used for the statistical modelling of time-to-event data. Survival data are characterized by a set of complete records, in which the time of the event is known; and a set of censored records, in which the event was known to have occurred in an interval. When survival data are spatially referenced, the spatial variation in survi...

The goals of this article are: (i) to understand how individual characteristics affect the likelihood of patients defaulting their pulmonary tuberculosis (PTB) treatment regimens; (ii) to quantify the predictive capacity of these risk factors; and (iii) to quantify and map spatial variation in the risk of defaulting. We used logistic regression mod...

Childhood malnutrition adversely affects short- and long-term health and economic well-being of children. Malnutrition is a global challenge and accounts for around 40% of under-five mortality in Ghana. Limited studies are available indicating determinants of malnutrition among children. This study investigates prevalence and determinants of malnut...

This article concerns the statistical modelling of emergency service response times. We apply advanced methods from spatial survival analysis to deliver inference for data collected by the London Fire Brigade on response times to reported dwelling fires. Existing approaches to the analysis of these data have been mainly descriptive; we describe and...

Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advanc...

Within an area of sub‐Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy that is currently utilized is reactive, such that a vaccination programme is initiated in a district once a predefined weekly incidence threshold has been exceeded. We report progress...

In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the inte...

This paper introduces an R package for spatial and spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo (MCMC) and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type....

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...

This paper introduces an R package for spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The modelling fram...

Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state space models, but offer an alternative to MCMC in situations where Bayesian inference must proceed via simulation. This paper introduces a new SMC method that uses adaptive MCMC kernels for particle dynamics. The proposed algorithm features an online stochasti...

We propose a new way of quantifying a team's strength of schedule for NCAA basketball. This strength of a schedule is defined as the number of games a team on the borderline of the annual national tournament would expect to win if they played that schedule. This gives a direct way of quantifying how well different teams have done relative to the sc...