A Two-Stage Approach to Spatio-Temporal Analysis with Strong and Weak Cross-Sectional Dependence

Abstract Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

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A Two Stage Approach to Spatiotemporal Analysis with Strong and Weak Cross-Sectional Dependence

SSRN Electronic Journal ◽

10.2139/ssrn.2393922 ◽

2014 ◽

Author(s):

Natalia Bailey ◽

Sean Holly ◽

M. Hashem Pesaran

Keyword(s):

Spatiotemporal Analysis ◽

Two Stage ◽

Cross Sectional ◽

Cross Sectional Dependence

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Transportation and logistics cluster competitive advantages in the U.S. regions: A cross-sectional and spatio-temporal analysis

Research in Transportation Economics ◽

10.1016/j.retrec.2016.07.028 ◽

2017 ◽

Vol 61 ◽

pp. 25-36 ◽

Cited By ~ 18

Author(s):

Indraneel Kumar ◽

Andrey Zhalnin ◽

Ayoung Kim ◽

Lionel J. Beaulieu

Keyword(s):

Temporal Analysis ◽

Competitive Advantages ◽

Cross Sectional ◽

Spatio Temporal ◽

The U.S

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A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

10.1101/2021.08.10.455613 ◽

2021 ◽

Author(s):

Diana M. P&eacuterez-Valencia ◽

Mar&iacutea Xos&eacute Rodr&iacuteguez-&Aacutelvarez ◽

Martin P. Boer ◽

Lukas Kronenberg ◽

Andreas Hund ◽

...

Keyword(s):

High Throughput ◽

Growth Curves ◽

Temporal Analysis ◽

Two Stage ◽

Temporal Noise ◽

Large Sets ◽

Field Phenotyping ◽

Corrected Data ◽

High Throughput Phenotyping ◽

Spatio Temporal

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

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Spatio-Temporal Analysis on Endemicity of Dengue Hemorrhagic Fever in Sragen District, Central Java

Childhood Stunting, Wasting, and Obesity, as the Critical Global Health Issues: Forging Cross-Sectoral Solutions ◽

10.26911/the7thicph.01.33 ◽

2020 ◽

Author(s):

Wiwik Setyaningsih ◽

Keyword(s):

Dengue Hemorrhagic Fever ◽

Hemorrhagic Fever ◽

Temporal Analysis ◽

Analysis Method ◽

Cross Sectional ◽

Central Java ◽

Main Variable ◽

Endemic Areas ◽

Spatio Temporal ◽

Cross Sectional Design

ABSTRACT Background: In recent decades, the incidence of dengue hemorrhagic fever (DHF) has risen significantly around the world. In Indonesia, studies reported 77.96 cases per 100,000 person-years in 2016 with the highest average number of cases in West Java. This study aimed to investigate the spatio-temporal analysis on endemicity of dengue hemorrhagic fever in Sragen Regency, Central Java. Subjects and Method: This was a descriptive study with cross-sectional design conducted in Sragen, Central Java from 2016 to 2018. A total of 1,354 cases was selected by total sampling. The main variable under study was the DHF incidence. The data were described by frequency distribution tables. Data were analyzed by spatio-temporal analysis method with overlay function using Geographic Information System (GIS). Results: The spatio-temporal analysis showed an increased DHF incidence in all sub-districts in Sragen Regency for three consecutive years 2016 to 2018. The highest incidence was 94 cases per 100,000 population in 2016. Sragen Regency was considered endemic areas of DHF. Conclusion: All sub-districts in Sragen Regency were endemic areas of DHF from 2016 to 2018. Keywords: spatio-temporal analysis, GIS, DHF, endemic Correspondence: Wiwik Setyaningsih. School of Health Polytechnics, Ministry of Health, Surakarta. Jl. Letjen. Sutoyo, Mojosongo, Surakarta, Central Java. Email: [email protected]. Mobile: +628122593472. DOI: https://doi.org/10.26911/the7thicph.01.33

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Twitter Usage Using Common Reference to Tinnitus

American Journal of Audiology ◽

10.1044/2020_aja-19-00055 ◽

2020 ◽

Vol 29 (2) ◽

pp. 206-217

Author(s):

Jianyuan Ni ◽

Monica L. Bellon-Harn ◽

Jiang Zhang ◽

Yueqing Li ◽

Vinaya Manchaiah

Keyword(s):

Nonprofit Organizations ◽

Temporal Trends ◽

Temporal Analysis ◽

Cross Sectional ◽

For Profit ◽

Common Reference ◽

Personal Experiences ◽

Twitter Users ◽

Sectional Analysis ◽

Share Information

Objective The objective of the study was to examine specific patterns of Twitter usage using common reference to tinnitus. Method The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results Around 70,000 tweets were identified and analyzed from May to October 2018. Of the 100 most active Twitter accounts, organizations owned 52%, individuals owned 44%, and 4% of the accounts were unknown. Commercial/for-profit and nonprofit organizations were the most common organization account owners (i.e., 26% and 16%, respectively). Seven unique tweets were identified with a reach of over 400 Twitter users. The greatest reach exceeded 2,000 users. Temporal analysis identified retweet outliers (> 200 retweets per hour) that corresponded to a widely publicized event involving the response of a Twitter user to another user's joke. Content analysis indicated that Twitter is a platform that primarily functions to advocate, share personal experiences, or share information about management of tinnitus rather than to provide social support and build relationships. Conclusions Twitter accounts owned by organizations outnumbered individual accounts, and commercial/for-profit user accounts were the most frequently active organization account type. Analyses of social media use can be helpful in discovering issues of interest to the tinnitus community as well as determining which users and organizations are dominating social network conversations.

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