Modelling of Spatio-temporal Zero Truncated Patterns in Infectious Disease Surveillance Data (original) (raw)

Spatio-temporal modelling of zero-truncated disease patterns

2015

This paper focuses on the spatio-temporal pattern of Leishmaniasis incidence in Afghanistan. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses (e.g., Yasui and Lele, 1997) . Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. The model was built on a foundation of the generalized estimating equations (Liang and Zeger, 1986). We illustrate the method using data from Afghanistan between 2003-2009. Since the data covers a period that overlaps with the US invasion of Afghanistan, the zero counts may be the result of no disease incidenc...

Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros

International journal of environmental research and public health, 2015

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level.

A Spatial-Temporal Approach to Differentiate Epidemic Risk Patterns

The purpose of disease mapping is to find spatial clustering and identify risk areas and potential epidemic initiators. Rather than relying on plotting either the case number or incidence rate, this chapter proposes three temporal risk indices: the probability of case occurrence (how often did uneven cases occur), the duration of an epidemic (how long did cases persist), and the intensity of a transmission (were the case of chronological significance). By integrating the three indicators using the local indicator of spatial autocorrelation (LISA) statistic, this chapter intends to develop a novel approach for evaluating spatial-temporal relationships with different risk patterns in the 2002 dengue epidemic, the worst outbreak in the past sixty years. With this approach, not only are hypotheses generated through the mapping processes in furthering investigation, but also procedures provided to identify spatial health risk levels with temporal characteristics.

Dissecting Spatial-Temporal Patterns of Disease Occurrence Using Spatial Statistics: The Daily Changes of the 2014-2015 Ebola Outbreak in West Africa

2015

Arthur Getis is Distinguished Professor of Geography Emeritus at San Diego State University. He has been president of several academic societies including the Western Regional Science Association (WRSA) and the University Consortium for Geographical Information Sciences (UCGIS). He has published papers with Keith Ord on the creation and development of local statistics, and books with Barry Boots on spatial pattern analysis. He has developed spatial clustering algorithms and filtering techniques. He has edited the Handbook of Applied Spatial Analysis (with Manfred Fischer). In addition, he has received the Walter Isard Distinguished Scholarship award and the Founder’s Medal of the Regional Science Association International (RSAI), and Distinguished Scholarship honors from the Association of American Geographers. He is a fellow of the WRSA, the RSAI, and UCGIS.

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

Symmetry

This article describes the application of spatial statistical epidemiological modeling and its inference and applies it to COVID-19 case data, looking at it from a spatial perspective, and considering time-series data. COVID-19 cases in Indonesia are increasing and spreading in all provinces, including Kalimantan. This study uses applied mathematics and spatiotemporal analysis to determine the factors affecting the constant rise of COVID-19 cases in Kalimantan. The spatiotemporal analysis uses the Geographically Temporally Weighted Regression (GTWR) model by developing a spatial and temporal interaction distance function. The GTWR model was applied to data on positive COVID-19 cases at a scale of 56 districts/cities in Kalimantan between the period of January 2020 and August 2021. The purpose of the study was to determine the factors affecting the cumulative increase in COVID-19 cases in Kalimantan and map the spatial distribution for 56 districts/cities based on the significant pre...

Analysing spatio-temporal autocorrelation with LISTA-Viz

International Journal of …, 2010

Many interesting analysis problems (for example, disease surveillance) would become more tractable if their spatio-temporal structure was better understood. Specifically, it would be helpful to be able to identify autocorrelation in space and time simultaneously. Some of the most commonly used measures of spatial association are LISA statistics, such as the Local Moran's I or the Getis-Ord Gi*, however these have not been applied to the spatio-temporal case (including many time steps) due to computational limitations. We have implemented a spatio-temporal version of the Local Moran's I, and claim two advances: First, we exploit the fact that there are a limited number of topological relationships present in the data to make Monte Carlo estimation of probability densities computationally practical, and thereby bypass the "curse of dimensionality". We term this approach "spatial memoization". Second, we developed a tool (LISTA-Viz) for interacting with the spatiotemporal structure uncovered by the statistics which contains a novel coordination strategy. The potential usefulness of the method and associated tool are illustrated by an analysis of the 2009 H1N1 pandemic, with the finding that there was a critical spatio-temporal "inflection point" at which the pandemic changed its character in the United States.

Inferring the dynamics of a spatial epidemic from time-series data

Bulletin of Mathematical Biology, 2004

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested

Spatio-temporal modelling of disease mapping of rates

2010

This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and autoregressive smoothing over the temporal dimension are developed. The model also accommodates the interaction between space and time. However, the effect of seasonal factors has not been previously addressed and in some applications (e.g., health conditions), these effects may not be negligible. The authors incorporate the seasonal effects of month and possibly year as part of the proposed model and estimate model parameters through generalized estimating equations. The model provides smoothed maps of disease risk and eliminates the instability of estimates in low-population areas while maintaining geographic resolution. They illustrate the approach using a monthly data set of the number of asthma presentations made by children to Emergency Departments (EDs) in the province of Alberta, Canada, during the period Résumé: Cet article considère les modèles linéaires mixtes généralisés (GLMM) pour l'analyse des variations géographique et temporelle l'incidence d'une maladie. Cette classe de modèles permet la présence d'effets aléatoires corrélés spatialement et des composantes temporelles aléatoires. Les auteurs développent des modèles utilisant un lissage autorégressif conditionnel pour la dimension spatiale et un lissage autorégressif pour la dimension temporelle. Ces modèles permettent aussi une interaction entre l'espace et le temps. Cependant, les effets des facteurs saisonniers ne sont pas encoreété considérés alors qu'ils peuventêtre non négligeables dans certaines applications (par exemple lesétats de santé). Les auteurs incorporent des effets saisonniers au niveau du mois et possiblement de l'année au modèle proposé et ils estiment ces paramètres en utilisant deséquations d'estimation généralisées. Le modèle permet d'obtenir une carte lisse du risque de maladie et ilélimine l'instabilité des estimateurs dans les régionsà faible population tout en maintenant la résolution géographique. Les auteurs illustrent leur approche en utilisant des données mensuelles représentant le nombre d'enfants se présentant aux salles d'urgence pour des raisons reliéesà l'asthme dans la province de l'Alberta (Canada) pendant la période 2001-04. La revue canadienne de statistique 38: 698-715; 2010

Modern perspectives on statistics for spatio-temporal data

Wiley Interdisciplinary Reviews: Computational Statistics, 2014

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially explicit processes that evolve over time. Although descriptive models that approach this problem from the second-order (covariance) perspective are important, many real-world processes are dynamic, and it can be more efficient in such cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The challenge with the specification of such dynamical models has been related to the curse of dimensionality and the specification of realistic dependence structures. Even in fairly simple linear/Gaussian settings, spatio-temporal statistical models are often over parameterized. This problem is compounded when the spatio-temporal dynamical processes are nonlinear or multivariate. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters and science-based parameterizations. Such models are best considered from a Bayesian perspective, with associated computational challenges. Spatio-temporal statistics remains an active and vibrant area of research.