Spatio-temporal modelling of disease incidence with missing covariate values

2014 ◽  
Vol 143 (8) ◽  
pp. 1777-1788 ◽  
Author(s):  
R. C. HOLLAND ◽  
G. JONES ◽  
J. BENSCHOP
Keyword(s):  
Risk Factors ◽  
Missing Values ◽  
Disease Incidence ◽  
Analysis Framework ◽  
Temporal Correlations ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Underlying Risk

SUMMARYThe search for an association between disease incidence and possible risk factors using surveillance data needs to account for possible spatial and temporal correlations in underlying risk. This can be especially difficult if there are missing values for some important covariates. We present a case study to show how this problem can be overcome in a Bayesian analysis framework by adding to the usual spatio-temporal model a component for modelling the missing data.

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Dayun Kang ◽  
Jungsoon Choi
Keyword(s):  
Scrub Typhus ◽  
Prediction Performance ◽  
Weather Data ◽  
Hierarchical Framework ◽  
Interaction Terms ◽  
Low Humidity ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Temporal Interaction

Scrub typhus, a bacterial, febrile disease commonly occurring in the autumn, can easily be cured if diagnosed early. However, it can develop serious complications and even lead to death. For this reason, it is an important issue to find the risk factors and thus be able to prevent outbreaks. We analyzed the monthly scrub typhus data over the entire areas of South Korea from 2010 through 2014. A 2-stage hierarchical framework was considered since weather data are covariates and the scrub typhus data have different spatial resolutions. At the first stage, we obtained the administrative-level estimates for weather data using a spatial model; in the second, we applied a Bayesian zero-inflated spatio-temporal model since the scrub typhus data include excess zero counts. We found that the zero-inflated model considering the spatio-temporal interaction terms improves fitting and prediction performance. This study found that low humidity and a high proportion of elderly people are significantly associated with scrub typhus incidence.

The spatio-temporal distribution and risk factors of thyroid cancer during rapid urbanization–A case study in China

2018 ◽  
Vol 630 ◽  
pp. 1436-1445 ◽  
Author(s):  
Xufeng Fei ◽  
Wanzhen Chen ◽  
Shuqing Zhang ◽  
Qingmin Liu ◽  
Zhonghao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Thyroid Cancer ◽  
Temporal Distribution ◽  
Rapid Urbanization ◽  
Spatio Temporal

scholarly journals ANALYSING DELAY IMPACT FROM POTENTIAL RISK FACTORS ON PROJECT DELIVERY OF OIL AND GAS PIPELINE: A CASE STUDY IN IRAQ

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
LAYTH A KRAIDI ◽  
Raj Shah ◽  
Wilfred Matipa ◽  
Fiona Fiona Borthwick
Keyword(s):  
Risk Factors ◽  
Oil And Gas ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Project Delivery ◽  
Analysis Framework ◽  
Potential Risk Factors ◽  
South Of Iraq ◽  
The Impact

The aim of this paper is to present the design and specifications of an integrated Delay Analysis Framework (DAF), which could be used to quantify the delay caused by the Risk Factors (RFs) in Oil and Gas Pipelines (OGPs) projects in a simple and systematic way. The main inputs of the DAF are (i) the potential list of RFs in the projects and their impact levels on the projects and the estimated maximum and minimum duration of each task. Monte Carlo Simulation integrated within @Risk simulator was the key process algorithm that used to quantify the impact of delay caused by the associated RFs. The key output of the DAF is the amount of potential delay caused by RFs in the OGP project. The functionalities of the developed DAF were evaluated using a case study of newly developed OGP project, in the south of Iraq. It is found that the case study project might have delayed by 45 days if neglected the consideration of the RFs associated with the project at the construction stage. The paper concludes that identifying the associated RFs and analysing the potential delay in advance will help in reducing the construction delay and improving the effectiveness of the project delivery by taking suitable risk mitigation measures.  

scholarly journals Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones

2016 ◽  
Vol 31 (2) ◽  
pp. 196-201 ◽  
Author(s):  
José Ruy Porto De Carvalho ◽  
Alan Massaru Nakai ◽  
José Eduardo B.A. Monteiro
Keyword(s):  
Daily Rainfall ◽  
Auxiliary Variable ◽  
Data Imputation ◽  
Temporal Modeling ◽  
Different Seasons ◽  
Versatile Technique ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Weather Stations ◽  
Spatio Temporal

Abstract Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio-temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio-temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio-temporal model proved to be a versatile technique, adapting to different seasons and dates.

scholarly journals Spatio Temporal Modelling for Government Policy the COVID-19 Pandemic in East Java

CAUCHY ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 218-226
Author(s):  
Atiek Iriany ◽  
Novi Nur Aini ◽  
Agus Dwi Sulistyono
Keyword(s):  
Government Policy ◽  
Large Scale ◽  
Cumulative Number ◽  
Data Modelling ◽  
Model Method ◽  
Daily Data ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Spatio Temporal ◽  
The Government

COVID-19 has cursorily spread globally. Just in four months, its status altered into a pandemic. In Indonesia, the virus epicenter is identified in Java. The first positive case was identified in West Java and later spread in all Java. The Large-scale Social Restrictions are seemingly inefficient as the SARS-CoV-2 transmission remains. As such, the government is struggling to find anticipatory policies and steps best to mitigate the transmission. In this particular article, we used a Spatio-temporal model method for the total COVID-19 cases in Java and forecasted the total cases for the next 14 days, allowing the stakeholders to make more effective policies. The data we were using were the daily data of the cumulative number of COVID-19 cases taken from www.covid19.go.id. Data modelling was conducted using a generalized spatio-temporal autoregressive model. The model acquired to model the COVID-19 cases in Java was the GSTAR(1)(1,0,0) model.

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