scholarly journals Comparison of Bayesian Spatio-temporal Models of Tuberculosis in Makassar, Indonesia

2021 ◽  
Vol 2123 (1) ◽  
pp. 012048
Author(s):  
Sukarna ◽  
Maya Sari Wahyuni ◽  
Rahmat Syam
Keyword(s):  
Relative Risk ◽  
High Risk ◽  
Goodness Of Fit ◽  
South Sulawesi ◽  
Temporal Models ◽  
Risk Of Disease ◽  
Temporal Modelling ◽  
Conditional Autoregressive ◽  
Spatio Temporal

Abstract South Sulawesi province ranks sixth-highest in tuberculosis (TB) in Indonesia. Makassar ranks the highest in South Sulawesi. Spatio-temporal modelling can identify the areas with high risk as well as the temporal relative risk of disease. We analysed the tuberculosis cases data from Makassar City Health Office for 15 districts over seven years from 2012 to 2018. Seven models of Bayesian Spatio-temporal (BST) Conditional Autoregressive (CAR) were applied by using the measures of goodness of fit (GOF) namely, DIC and WAIC. The results showed that BST CAR localised model with G = 3 has the lowest DIC and BST CAR adaptive has the lowest WAIC. Based on the preferred model (Bayesian ST CAR localised with G=3), Panakukang district had the highest relative risk of TB in 2012, 2013, and 2014, while Makassar district had the highest relative risk of TB in 2015, 2016, and 2017. Mamajang had the highest relative risk of TB in 2018.

scholarly journals ASSOCIATIONS OF BLOOD GROUPS ANTIGENS OF AB0 AND RHESUS SYSTEMS WITH THE DEVELOPMENT OF CHRONIC KIDNEY DISEASE, GLOMERULONEPHRITIS

2014 ◽  
pp. 35-43
Author(s):  
V. Kaminskyy ◽  
L. Kovalchuk
Keyword(s):  
Chronic Kidney Disease ◽  
Relative Risk ◽  
High Risk ◽  
Early Stage ◽  
Blood Groups ◽  
Sex Dimorphism ◽  
Risk Of Disease ◽  
The Common ◽  
High Chance ◽  
Healthy Persons

Introduction. Finding of biological markers of genetic predisposition to the formation of glomerulonephritis (GN) will promote prediction the probability of its development still at an early stage and provide the growth of preventive direction of medicine. The purpose of the study is to evaluate the risk of GN development by antigens of AB0 and rhesus (Rh) blood groups. Materials and methods. The study included 434patients with GN(242M, 192F, aged 37.56 ± 13.01y). 1428 healthy persons was surveyed to determine the distribution of phenotypes of AB0 and Rh blood groups in the population. Results. The total value of the relative risk of GN development in all Rh–negative carriers ABprevailed by 2.34 times in the same Rh–positive. The total value of the relative risk of disease appearance in Rh–negative individuals prevailed in the same Rh–positive according to gender: in men with A and AB – 6.43 and 4.16 times, respectively, in women with B and AB – 9.34 and 2.15 times, respectively. In all patients, the common feature was a high chance of getting sick by GN in carriers phenotype AB Rh– versus 0 Rh–. Conclusions. The sex dimorphism of hereditary predisposition markers for GN is proved: men with phenotypes A Rh– and AB Rh–, women with B Rh–, AB Rh– and AB Rh+ have high risk to be ill. The persons of both sexes with phenotype 0 Rh–, as well as men with B Rh– and women with A Rh– and B Rh+ may be resistant to disease.

Stochastic spatio-temporal modelling of African swine fever spread in the European Union during the high risk period

2013 ◽  
Vol 108 (4) ◽  
pp. 262-275 ◽  
Author(s):  
Annette Nigsch ◽  
Solenne Costard ◽  
Bryony A. Jones ◽  
Dirk U. Pfeiffer ◽  
Barbara Wieland
Keyword(s):  
European Union ◽  
High Risk ◽  
African Swine Fever ◽  
The European Union ◽  
Risk Period ◽  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING

2021 ◽  
Vol 14 (2) ◽  
pp. 158-169
Author(s):  
Aswi Aswi ◽  
Andi Mauliyana ◽  
Muhammad Arif Tiro ◽  
Muhammad Nadjib Bustan
Keyword(s):  
Relative Risk ◽  
Count Data ◽  
Spatial Modeling ◽  
South Sulawesi ◽  
The World ◽  
Bayesian Spatial Modeling ◽  
Risk Areas ◽  
Mean And Variance ◽  
Conditional Autoregressive ◽  
The Mean

The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG (1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies.

scholarly journals Multiple Sclerosis incidence rate in southern Iran: A Bayesian epidemiological study

2020 ◽  
Author(s):  
Naeimehossadat Asmarian ◽  
Zahra Sharafi ◽  
Amin Mousavi ◽  
Reis Jacques ◽  
Ibon Tamayo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Vitamin D ◽  
Relative Risk ◽  
Incidence Rate ◽  
Vitamin D3 ◽  
Incidence Rates ◽  
Fars Province ◽  
Southern Iran ◽  
Temporal Models ◽  
Spatio Temporal

Abstract Background: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanism and clinical impact on young people. The prevalence of this disease in Iran is reported to be 5.3 to 74.28 per 100000 cases. Due to high prevalence of this disease in Fars province, this study aimed to assess the distribution of MS in this region in southern Iran by evaluating its covariates.Method: Data from 5,468 patients diagnosed with MS were collected, according to the McDonald’s criteria, which was reported by the MS Society of Fars from 1991 until 2016. Bayesian spatio-temporal models was also used to describe MS incidence in Fars province. We also investigated the association between overall MS incidence rate and the overall percentage of vitamin D intake, smokers in the population as well as the overall percentage of people with normal BMI as well as alcohol consumption in a population from 1991 until 2016 by Besag, York and Mollie's (BYM) model.Results: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000 population. The highest relative risk was estimated at 1.8 in the city of Shiraz, the capital of Fars province while the lowest relative risk was estimated at 0.11 in Zarindasht County in southern Fars. The percentages of vitamin D3 intake was significantly associated with the incidence of MS. Although 1% increase in Vitamin D3 intake is associated with 2% decrease in the risk of MS, 1% increase in smoking is associated with 16% increase in the risk of MS, respectively.Conclusion: Spatial analysis of MS showed low incidence rate of this disease in the south and south east of Fars province, which is due to the effect of different covariates. As suggested by previous studies, vitamin D and smoking among all covaiates might be associated with high incidence of MS.

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

scholarly journals Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach

2021 ◽  
Vol 248 ◽  
pp. 118192
Author(s):  
Guido Fioravanti ◽  
Sara Martino ◽  
Michela Cameletti ◽  
Giorgio Cattani
Keyword(s):  
Temporal Modelling ◽  
Spatio Temporal

scholarly journals Improved inference for areal unit count data using graph-based optimisation

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Duncan Lee ◽  
Kitty Meeks ◽  
William Pettersson
Keyword(s):  
Random Effects ◽  
Count Data ◽  
Prior Distribution ◽  
Disease Surveillance ◽  
Sharing Rule ◽  
Markov Random ◽  
Spatial Correlation Structure ◽  
Conditional Autoregressive ◽  
Spatio Temporal ◽  
Special Case

AbstractSpatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

Sign in / Sign up

Export Citation Format

Share Document