scholarly journals Estimating relative risk for dengue disease in Peninsular Malaysia using INLA

2017 ◽  
Vol 13 (4) ◽  
pp. 721-727 ◽  
Author(s):  
Nurul Syafiah Abd Naeeim ◽  
Nuzlinda Abdul Rahman
Keyword(s):  
Relative Risk ◽  
Disease Mapping ◽  
Peninsular Malaysia ◽  
Space Time ◽  
Dengue Disease ◽  
Temporal Effects ◽  
Space And Time ◽  
Time Interaction ◽  
Spatio Temporal ◽  
Mapping Models

Study in spatio-temporal disease mapping models give a great worth in epidemiology, in describing the pattern of disease incidence across geographical space and time. This paper studies generalized linear mixed models (GLMM) for the analysis of spatial and temporal variability of dengue disease rates. For spatio-temporal study, the models accommodate spatially correlated random effects as well as temporal effects together with the space time interaction. The space time interaction is used to capture any additional effects that are not explained by the main factors of space and time. However, as study including time dimension is quite complex for disease mapping, the temporal effects that only relate to structured and unstructured time pattern are considered in these models as initial screening in studying disease pattern and time trend. The models are fitted within a hierarchical Bayesian framework using Integrated Nested Laplace Approximation (INLA) methodology. For this study, there are three main objectives. First, to choose the best model that represent the disease phenomenon. Second, to estimate the relative risk of disease based on the model selected and lastly, to visualize the risk spatial pattern and temporal trend using graphical representation. The models are applied to monthly dengue fever data in Peninsular Malaysia reported to Ministry of Health Malaysia for year 2015 by district level.

scholarly journals Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

2019 ◽  
Vol 8 (2) ◽  
pp. 72 ◽  
Author(s):  
Yi Qiang ◽  
Nico Van de Weghe
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Space Time ◽  
Unified Framework ◽  
Temporal Modeling ◽  
Multi Scale ◽  
Space And Time ◽  
Pyramid Model ◽  
Temporal And Spatial ◽  
Spatio Temporal

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

On fitting spatio-temporal disease mapping models using approximate Bayesian inference

2014 ◽  
Vol 23 (6) ◽  
pp. 507-530 ◽  
Author(s):  
María Dolores Ugarte ◽  
Aritz Adin ◽  
Tomas Goicoa ◽  
Ana Fernandez Militino
Keyword(s):  
Bayesian Inference ◽  
Disease Mapping ◽  
Spatio Temporal ◽  
Approximate Bayesian ◽  
Mapping Models ◽  
Approximate Bayesian Inference

scholarly journals On estimation the relative risk of small area and visualization spatio-temporal map

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoxiao Song ◽  
Yan Li ◽  
Le Cai ◽  
Wei Liu ◽  
Wenlong Cui
Keyword(s):  
Relative Risk ◽  
Small Area ◽  
Empirical Bayes ◽  
Disease Risk ◽  
Risk Estimation ◽  
Risk Index ◽  
Disease Mapping ◽  
R Software ◽  
Relative Risks ◽  
Spatio Temporal

ObjectiveThe purpose is to propose a serial of approach for estimation for disease risk for ILI in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with HTML format.IntroductionDisease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. Recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. This abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them.MethodsAll the data are from a syndromic surveillance and real-time early warning system in the Yunnan province in the China. For brief introduction aim, we are using the ILI (Influenza-like illness) data in December 2017 in one county. The relative risks of disease in small area are including: raw incidence, a standardized morbidity ratio (SMR), Empirical Bayes smoothing estimation relative risk (EB-RR) and the Besag-York-Mollio model (BYM). The incidence in each small area is common used for descriptive the risk but fail to comparable directly since the different population at risk in each area. SMR is a good way to deal with this incomparability. But SMR can give rise to imprecisely estimate in areas with small populations. Empirical Bayes estimation approach has been used for smoothing purpose and can be seen as a compromise between relative risks and P-values. However, all above approaches are inept to have spatial or spatio-temporal structure in mind. BYM based the Bayesian inference can handle both the area-specific spatial structured component (such as intrinsic conditional autoregressive component) and the exchangeable random effect (unstructured component). All the analyses are implemented in the R software with INLA package (http://www.r-inla.org). The outcome of relative risk estimation with visual way and interactive maps showing are using ggplot2 and leaflet packages.Results1, the spatio-temporal raw cases of ILI from 2017/12/01 to 2017/12/31 is Fig.12. the SMR and EB-RR estimation RR of ILI are in Fig.2 and Fig.33. the most excited is the interactive visualization with HTML format for all the risk indexes is visited http://rpubs.com/ynsxx/424814 in detail. And the screenshot is Fig.4ConclusionsSmall area disease risk estimation is important for disease prevention and control. The faster function of computer with power R software can lead to advance in disease mapping, allowing for complex spatio-temporal models and communicate the results with visualization way. 

An Application of Space-Time Analysis to Improve the Epidemiological Understanding of the Papaya-Papaya Yellow Crinkle Pathosystem

2007 ◽  
Vol 8 (1) ◽  
pp. 65
Author(s):  
Paul D. Esker ◽  
Karen S. Gibb ◽  
Philip M. Dixon ◽  
Forrest W. Nutter
Keyword(s):  
Strong Evidence ◽  
Pattern Analysis ◽  
Space Time ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Incidence Data ◽  
Time Interaction ◽  
Spatio Temporal ◽  
Time Point ◽  
Contagion Process

Yellow crinkle disease of papaya is a serious threat to papaya production in Australia. Space-time point pattern analysis was used to study the spatial and temporal dependence of two phytoplasma strains that cause yellow crinkle: tomato big bud (TBB) and sweet potato little leaf V4 (SPLL-V4). Incidence data for both phytoplasma strains were obtained from a field study conducted in Katherine, NT, Australia, between January 1996 and May 1999. The primary ecological and epidemiological question of interest was to elucidate the scale of spatial or spatio-temporal aggregation of phytoplasma-infected papaya plants. The hypothesis was that there would be a contagion process, where TBB- and SPLL-V4-infected papaya would be aggregated and not random. To test this hypothesis, a point pattern spatial analysis using Monte Carlo simulation was initially applied to the incidence data. Results of this analysis suggested that SPLL-V4 infected papaya plants displayed aggregation with spatial dependence up to 30 m (10 to 15 plants along or across rows), whereas there was not strong evidence to suggest that TBB-infected papaya plants were aggregated. However, when a space-time point pattern analysis was subsequently used to simultaneously test for the interaction between space and time, there was strong evidence (P < 0.01 for SPLL-V4 and P < 0.10 for TBB) to suggest a space-time interaction for both SPLL-V4 and TBB. For SPLL-V4, a space-time risk window of approximately 10 months and 20 m was detected, whereas for TBB, this risk window was 5 months and 10 m. The results of these studies support the hypothesis that papaya infection by both phytoplasma strains appears to be the result of a contagion process, providing support for the contention that insect vectors are the most likely mechanism for acquisition, dispersal, and transmission. Accepted for publication 26 April 2007. Published 26 July 2007.

СПЕЦИФИКА ФОРМИРОВАНИЯ ПЕРЦЕПТУАЛЬНОГО ПРОСТРАНСТВА И ВРЕМЕНИ В РАССКАЗЕ И. С. ТУРГЕНЕВА «СОН»

2020 ◽  
Author(s):  
Elena Skudnyakova
Keyword(s):  
Space Time ◽  
Perceptual Space ◽  
Main Character ◽  
Space And Time ◽  
The Fantastic ◽  
Spatio Temporal

В статье рассматривается специфика формирования перцептуального пространства и времени в рассказе И. С. Тургенева «Сон». Автор доказывает, что специфика обусловлена присутствием в произведении категории фантастического и особого типа личности главного героя, который является носителем фантастического (таинственные сны). Установлено, что пространственно-временные искажения перцептуальной сферы происходят в его сознании и подсознании. В ходе анализа выявлено, что сфера перцептуального пространства-времени сначала расширяется (фантастический сон - сон-галлюцинация - сон наяву), а потом органично включается в реальную действительность. Подчеркивается особая значимость сна-галлюцинации, который активизирует динамику последующего событийного ряда.The article discusses the formation of perceptual space and time in the story of I. S. Turgenev «Dream». The author proves that the specificity is due to the presence of the category of fantastic in the work and a special type of personality of the main character, who is the bearer of the fantastic (mysterious dreams). It has been established that the spatio-temporal distortions of the perceptual sphere occur in the consciousness and subconscious of the protagonist. The analysis revealed that the sphere of perceptual space-time first expands (a fantastic dream - a dream-hallucination - a dream in reality), and then it is naturally included in reality. The author stresses the significance of sleep-hallucination which activates the dynamics of the subsequent series of events.

scholarly journals Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load : a spatio-temporal investigation in Vaud state, Switzerland

2021 ◽  
Author(s):  
Anaïs Ladoy ◽  
Onya Opota ◽  
Pierre-Nicolas Carron ◽  
Idris Guessous ◽  
Séverine Vuilleumier ◽  
...  
Keyword(s):  
Viral Load ◽  
Relative Risk ◽  
Urban Areas ◽  
High Viral Load ◽  
Space Time ◽  
Public Health Decision ◽  
Viral Loads ◽  
Health Decision ◽  
Spatio Temporal ◽  
Number Of Individuals

AbstractTo understand the geographical and temporal spread of SARS-CoV-2 during the first wave of infection documented in the canton of Vaud, Switzerland, we analysed clusters of positive cases using the precise place of residence of 33’651 individuals tested (RT-PCR) between January 10 and June 30, 2020. We identified both space-time (SaTScan) and transmission (MST-DBSCAN) clusters; we estimated their duration, their transmission behavior (emergence, growth, reduction, etc.) and relative risk. For each cluster, we computed the within number of individuals, their median age and viral load.Among 1’684 space-time clusters identified, 457 (27.1%) were significant (p ≤ 0.05), i.e. harboring a higher relative risk of infection, as compared to other regions. They lasted a median of 11 days (IQR 7-13) and included a median of 12 individuals per cluster (IQR 5-20). The majority of significant clusters (n=260; 56.9 %) had at least one person with an extremely high viral load (above 1 billion copies/ml). Those clusters were considerably larger (median of 17 infected individuals, p < 0.001) than clusters with subjects showing a viral load lower than 1 million copies/ml (median of 3 infected individuals). The highest viral loads were found in clusters with the lowest average age, while clusters with the highest average age had low to middle viral load. Interestingly, in 20 significant clusters the viral load of three first cases were all below 100’000 copies/ml suggesting that subjects with less than 100’000 copies/ml may still have been contagious. Noteworthy, the dynamics of transmission clusters made it possible to identify three diffusion zones, which mainly differentiated rural from urban areas, the latter being more prone to last and spread in a new nearby clusters.The use of geographic information is key for public health decision makers to mitigate the spread of the virus. This study suggests that early localization of clusters help implementing targeted protective measures limiting the spread of the SARS-CoV-2 virus.

Adjusting the Knox test by accounting for spatio-temporal crime risk heterogeneity to analyse near-repeats

2020 ◽  
pp. 147737082090510
Author(s):  
Álvaro Briz-Redón ◽  
Francisco Martínez-Ruiz ◽  
Francisco Montes
Keyword(s):  
Preventive Measures ◽  
Statistical Tool ◽  
Temporal Effects ◽  
Space And Time ◽  
Knox Test ◽  
Temporal Intervals ◽  
Spatio Temporal ◽  
Crime Risk ◽  
Near Repeat ◽  
The City

The near-repeat phenomenon usually occurs with any crime. Hence, to implement preventive measures, it is of great interest to figure out at which spatio-temporal scale crimes are more likely to be repeated by offenders. The Knox test is the most used statistical tool for evaluating the presence of the near-repeat phenomenon given a dataset of crimes that are located in space and time. The classic version of this test assumes that crime risk is homogeneous in both space and time, although this assumption rarely holds in reality. Therefore, the main goal of this article is to highlight the necessity of adjusting the standard version of the Knox test, including spatial and temporal effects that allow for the consideration of crime risk heterogeneity. In this regard, a methodology that has already been proposed for addressing this issue is described and adapted. This methodology is then put into practice through a dataset of burglaries recorded in the city of Valencia (Spain) in 2016 and 2017. The results yielded by both versions (standard and adjusted) of the Knox test confirm that the near-repeat phenomenon took place for the burglaries that occurred in Valencia during the period under investigation. However, using the adjusted version of the Knox test leads to a reduction in the number of spatio-temporal intervals that are declared as statistically significant. This fact should be born in mind before making decisions on preventive measures.

Methods for Space-Time Analysis and Modeling

2013 ◽  
Vol 4 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Eric Delmelle ◽  
Changjoo Kim ◽  
Ningchuan Xiao ◽  
Wei Chen
Keyword(s):  
Geographical Information Systems ◽  
Optimization Problems ◽  
Critical Distance ◽  
Space Time ◽  
Geographical Information ◽  
Temporal Data ◽  
Space And Time ◽  
Time Modeling ◽  
Spatio Temporal ◽  
The Impact

With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

DENGUE DISEASE MAPPING IN BANDUNG, INDONESIA: AN ANALYSIS BASED ON POISSON-GAMMA, LOG-NORMAL, BYM AND MIXTURE MODELS

2016 ◽  
Vol 78 (6-5) ◽  
Author(s):  
Farah Kristiani ◽  
Nor Azah Samat ◽  
Sazelli Ab Ghani
Keyword(s):  
Relative Risk ◽  
Mixture Models ◽  
Disease Mapping ◽  
Viral Disease ◽  
Aggregated Data ◽  
Dengue Disease ◽  
The World ◽  
Disease Risks ◽  
Log Normal ◽  
And Control

Dengue is the most rapidly spreading mosquitoes-borne viral disease in the world, especially in Bandung, Indonesia. This disease can be controlled if detected early. Therefore, in order to prevent and control this disease before it occurs, government and society must be cooperative to eradicate this dangerous disease. The statistical model used in the study of disease mapping can be considered as an important contribution. In this paper, the relative risk estimations using the Poisson-gamma, Log-normal, Besag, York and Mollié (BYM) and Mixture models for Bandung municipality will be investigated. In this study, the aggregated data of observed dengue data from Bandung, Indonesia from the year 2013 will be analyzed. The estimated relative risk will be displayed in tables and maps to obtain the clearer depictions of disease risks distribution in each area.

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