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.

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.

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 Spatio-temporal patterns of scrub typhus in mainland China, 2006-2017

2019 ◽  
Vol 13 (12) ◽  
pp. e0007916 ◽  
Author(s):  
Yujuan Yue ◽  
Dongsheng Ren ◽  
Xiaobo Liu ◽  
Yujiao Wang ◽  
Qiyong Liu ◽  
...  
Keyword(s):  
Scrub Typhus ◽  
Mainland China ◽  
Temporal Patterns ◽  
Spatio Temporal

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

Exploring Weather Data to Predict Activity Attendance in Event-based Social Network

2021 ◽  
Vol 15 (2) ◽  
pp. 1-25
Author(s):  
Jifeng Zhang ◽  
Wenjun Jiang ◽  
Jinrui Zhang ◽  
Jie Wu ◽  
Guojun Wang
Keyword(s):  
Real Life ◽  
Prediction Performance ◽  
Weather Data ◽  
Gradient Boosting ◽  
User Interest ◽  
Weather Factors ◽  
Event Duration ◽  
Outdoor Activities ◽  
Weather Factor ◽  
Event Based

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

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
Sign in / Sign up

Export Citation Format

Share Document