1,721,051 research outputs found
Hierarchical space-time modelling of epidemic dynamics: an application to measles outbreaks
No full textHow infectious diseases spread in space and time is an important question
that has received considerable theoretical attention. There are, however, few
empirical studies to support theoretical approaches, because data is scarce. In this
paper we propose to model the epidemic spread of measles in the London boroughs
between 1960 and 1970 by an extension of the Kriged Kalman filter (Mardia et
al., 1998) to count data. Results show the flexibility of our approach in describing
complex spatio-temporal dynamic
Semiparametric zero-inflated Poisson models with application to animal abundance studies
No full textMortalita' ed inquinamento atmosferico a Philadelphia: un approccio secondo i modelli dinamici lineari generalizzati
No full text
An interchangeable approach for modelling spatio-temporal count data
No full textWe describe a model-based approach to analyse space–time count data. Such data can arise as a number of time
series of counts, each representing a specific geographical area, i.e. as spatial time series, or as a number of spatial
maps at different time points, i.e. as temporal spatial processes. We propose a Bayesian hierarchical formulation
capable of embracing both cases, with principal kriging functions combined with latent parameters having prior
distributions able to deal with spatial/temporal dependence. The methodology is applied to monitoring problems
in environmental and epidemiological applications
Hierarchical space-time modelling of epidemic dynamics: an application to measles outbreaks
No full textHow infectious diseases spread in space and time is an important question
that has received considerable theoretical attention. There are, however, few
empirical studies to support theoretical approaches, because data is scarce. In this
paper we propose to model the epidemic spread of measles in the London boroughs
between 1960 and 1970 by an extension of the Kriged Kalman filter (Mardia et
al., 1998) to count data. Results show the flexibility of our approach in describing
complex spatio-temporal dynamic
Transfer function modelling strategy for combining evidence on air pollution and daily mortality
No full textA model for space-time threshold exceedances with an application to extreme rainfall
Full text linkIn extreme value studies models for observations exceeding a fixed high threshold have the advantage of exploiting the available extremal information,
while avoiding bias from low values. In the context of space-time data, the challenge is to develop models for threshold
exceedances that account for both spatial and temporal dependence. We address this issue through a modelling approach that embeds
spatial dependence within a time series formulation. The model allows for different forms of limiting dependence in the spatial and temporal
domains as the threshold level increases. In particular, temporal asymptotic independence is assumed, as this is often supported by empirical
evidence, especially in environmental applications, while both asymptotic dependence and asymptotic independence are considered for the spatial domain.
Inference from the observed exceedances is carried out through a combination of pairwise likelihood and a censoring mechanism.
For those model specifications for which direct maximization of the censored pairwise likelihood is unfeasible, we propose an indirect
inference procedure which leads to satisfactory results in a simulation study. The approach is applied to a dataset of rainfall amounts
recorded over a set of weather stations in the North Brabant province of the Netherlands. The application shows that the range of extremal
patterns that the model can cover is wide and that it has a competitive performance with respect to an alternative
existing model for space-time threshold exceedances
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