Bayesian spatial-temporal autologistic regression model on dengue hemorrhagic fever in east Java, Indonesia

Astutik, Suci; Rahayudi, Bayu; Iskandar, Agustin; Fitriani, Rahma

doi:10.12988/ams.2013.13038

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Alternative models included estimation of relative risk for the transmission of dengue disease based on discrete time and space via a susceptible–infective-recovered model for human populations; susceptible–infective model for mosquito populations (SIR-SI) [ 54 ], prediction of spread of DF using Bayesian maximum entropy (BME) [ 30 , 55 , 56 ], and spatio-temporal quasi-Poisson model based on a DLNM (distributed lag non-linear model) [ 49 ], STARM (spatial–temporal autologistic regression model) [ 34 ], hierarchical model with adaptive natural cubic spline [ 32 ], a semi-parametric Bayesian STAR (structured additive regression) model [ 31 ] and a transmission model based on Ross–Macdonald theory [ 45 ]. The analytical methods used across all included studies are summarised in Supplementary Table S3 and summary of the structure of the spatio-temporal models discussed in the reviewed paper can be seen in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…STARM: The STARM model is an extension of an autologistic regression model that includes covariates, spatial and temporal dependence simultaneously. This model has been applied to predict the association between the incidence of endemic dengue and rainfall using a Bayesian method [ 34 ]. For binary data that are measured repeatedly on a spatial lattice, STARM can be very beneficial [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

“…Precipitation and temperature were the most common and most commonly significant predictors [ 32 , 49 , 52 ]. Most studies found a positive significant association with precipitation [ 34 , 35 , 37 , 50 ], although one study found a negative correlation with precipitation 4–6 months previously [ 48 ]. These more complex associations depend on local seasonal patterns.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

et al. 2018

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Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

et al. 2018

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“…Other studies have used 10 sub-districts [ 23 , 24 ], 30 sub-districts [ 25 ], and 38 districts [ 26 ] for modelling dengue fever in Indonesia. Most of these papers used ICAR for the spatial structured random effect component.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the impact of a small number of areas on spatial estimation

et al. 2020

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Background There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study. Methods Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered. Results The simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised $$G = 2$$ G = 2 models performed similarly and better than the independent and localised $$G = 3$$ G = 3 models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice. Conclusion Detecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.

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“…The spatial distribution [19] and the location of the households can be observed accurately using the map or hot spot analysis [20]. Spatially, the high clusters of a statistically significant high score are shown by the hot spots, while the low clusters are indicated by the cold spots [21][22][23][24].…”

Section: Slum Distribution Pattern In Palembang Citymentioning

confidence: 99%

Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

2020

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The paper aims to describe the population characteristics and the distribution patterns of slums in Palembang City. The research employs a quantitative method with 382 respondents. The data are analyzed using cross-tabulation of IBM SPSS 23 to know the population characteristics. Meanwhile, the distribution patterns of slums are analyzed by observing the sample distribution through the proportional random sampling technique. It is carried out by calculating the number of buildings of each area and noting the coordinates of each sample using GPS essentials application. The data are recorded and inserted into the sample spots on the map, which were then analyzed using the High-Low Clustering Report of Getis Ord General Gi*, to see the distribution pattern, especially the cold spot and hot spot, through ArcMap 103 program. The research found that non-migrant married Moslems dominate the population of Palembang city, with the average occupation is labor or manual worker. The slum distribution forms a low cluster pattern, meaning that it has a low value. The value is due to the government’s effort to manage the city and the development of the market sector, limiting the slum distribution. Getis Ord Gi* analysis revealed that the slum area in the city center and within a dense population is a cold spot (low cluster), while those far from the city center yet are still crowded are hot spots (high cluster).

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Bayesian spatial-temporal autologistic regression model on dengue hemorrhagic fever in east Java, Indonesia

Cited by 6 publications

References 18 publications

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

Evaluating the impact of a small number of areas on spatial estimation

Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

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