35,519 research outputs found
Estimating Under Five Mortality in Space and Time in a Developing World Context
Accurate estimates of the under-5 mortality rate (U5MR) in a developing world
context are a key barometer of the health of a nation. This paper describes new
models to analyze survey data on mortality in this context. We are interested
in both spatial and temporal description, that is, wishing to estimate U5MR
across regions and years, and to investigate the association between the U5MR
and spatially-varying covariate surfaces. We illustrate the methodology by
producing yearly estimates for subnational areas in Kenya over the period 1980
- 2014 using data from demographic health surveys (DHS). We use a binomial
likelihood with fixed effects for the urban/rural stratification to account for
the complex survey design. We carry out smoothing using Bayesian hierarchical
models with continuous spatial and temporally discrete components. A key
component of the model is an offset to adjust for bias due to the effects of
HIV epidemics. Substantively, there has been a sharp decline in U5MR in the
period 1980 - 2014, but large variability in estimated subnational rates
remains. A priority for future research is understanding this variability.
Temperature, precipitation and a measure of malaria infection prevalence were
candidates for inclusion in the covariate model.Comment: 36 pages, 11 figure
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
Model term selection for spatio-temporal system identification using mutual information
A new mutual information based algorithm is introduced for term selection in spatio-temporal models. A generalised cross validation procedure is also introduced for model length determination and examples based on cellular automata, coupled map lattice and partial differential equations are described
A Spatio-Temporal Point Process Model for Ambulance Demand
Ambulance demand estimation at fine time and location scales is critical for
fleet management and dynamic deployment. We are motivated by the problem of
estimating the spatial distribution of ambulance demand in Toronto, Canada, as
it changes over discrete 2-hour intervals. This large-scale dataset is sparse
at the desired temporal resolutions and exhibits location-specific serial
dependence, daily and weekly seasonality. We address these challenges by
introducing a novel characterization of time-varying Gaussian mixture models.
We fix the mixture component distributions across all time periods to overcome
data sparsity and accurately describe Toronto's spatial structure, while
representing the complex spatio-temporal dynamics through time-varying mixture
weights. We constrain the mixture weights to capture weekly seasonality, and
apply a conditionally autoregressive prior on the mixture weights of each
component to represent location-specific short-term serial dependence and daily
seasonality. While estimation may be performed using a fixed number of mixture
components, we also extend to estimate the number of components using
birth-and-death Markov chain Monte Carlo. The proposed model is shown to give
higher statistical predictive accuracy and to reduce the error in predicting
EMS operational performance by as much as two-thirds compared to a typical
industry practice
Modeling highly pathogenic avian influenza transmission in wild birds and poultry in West Bengal, India.
Wild birds are suspected to have played a role in highly pathogenic avian influenza (HPAI) H5N1 outbreaks in West Bengal. Cluster analysis showed that H5N1 was introduced in West Bengal at least 3 times between 2008 and 2010. We simulated the introduction of H5N1 by wild birds and their contact with poultry through a stochastic continuous-time mathematical model. Results showed that reducing contact between wild birds and domestic poultry, and increasing the culling rate of infected domestic poultry communities will reduce the probability of outbreaks. Poultry communities that shared habitat with wild birds or those indistricts with previous outbreaks were more likely to suffer an outbreak. These results indicate that wild birds can introduce HPAI to domestic poultry and that limiting their contact at shared habitats together with swift culling of infected domestic poultry can greatly reduce the likelihood of HPAI outbreaks
Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions
Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalisation or mortality data. However, this approach limits the analysis to individuals characterised by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population.
We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO2 concentration integrating several data sources characterised by different spatio-temporal resolution; in the second stage we link the concentration to the β2-agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach
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