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5. Spatio-temporal spread of COVID-19 and its associations with socioeconomic, demographic and environmental factors in England: A Bayesian hierarchical spatio-temporal model

Author

Yin, Xueqing, Aiken, John M., Harris, Richard, and Bamber, Jonathan L.

Subjects
Statistics - Applications
Abstract

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aims to investigate the spatio-temporal spread of COVID-19 infection rate in England, and examine its associations with socioeconomic, demographic and environmental risk factors. Using weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England, we developed a Bayesian hierarchical spatio-temporal model to predict the COVID-19 infection rates and investigate the influencing factors. The analysis showed that our model outperformed the ordinary least squares (OLS) and geographically weighted regression (GWR) models in terms of prediction accuracy. The results showed that the spread of COVID-19 infection rates over space and time was heterogeneous. Hotspots of infection rate exhibited inconsistent clustered patterns over time. Among the selected risk factors, the annual household income, unemployment rate, population density, percentage of Caribbean population, percentage of adults aged 45-64 years old, and particulate matter concentrations were found to be positively associated with the COVID-19 infection rate. The findings assist policymakers in developing tailored public health interventions for COVID-19 prevention and control., Comment: 19 pages, 5 figures

Published
2023

6. 4DModeller (fdmr): A Comprehensive R Package for Spatio-Temporal Modelling

Author

Yin, Xueqing, Aiken, John M., and Bamber, Jonathan L.

Abstract

Spatio-temporal data analysis is crucial in many research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing 4DModeller (fdmr), a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The software package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution, highdimensional spatio-temporal data. Furthermore, "fdmr" provides intuitive and interactive visual analytics tools that facilitate the exploration of data patterns across both space and time. This paper aims to introduce the "fdmr" package, and outline its core modelling framework through an example study on the spread of COVID-19 infection rates in England from 19 December, 2020 to 20 March, 2021.

Published
2023

7. Risk estimation and discontinuity identification in Bayesian disease mapping

Author

Yin, Xueqing

Subjects
HA Statistics
Abstract

Disease mapping is the field of epidemiology that estimates the spatial or spatio-temporal pattern in disease risk. Approaches in this field are generally based on data collected on a set of non-overlapping areal units that comprise the study region, and typically utilise counts of the numbers of disease cases within each areal unit. Conditional autoregressive (CAR) models are commonly used to capture the spatial autocorrelation present in areal unit disease count data. The spatial correlation structure that is induced by these models is typically determined by a neighbourhood matrix based on geographical adjacency, which enforces spatial correlation between geographically neighbouring areas and assumes a spatially smooth risk surface. However this may not be realistic in practice, because some pairs of neighbouring areas are likely to exhibit vastly different disease risks. Therefore the aim of this thesis is to develop methodology that allows for discontinuities in the spatial risk pattern when estimating disease risk. The first two models proposed are in a purely spatial setting and account for discontinuities by identifying spatial clusters of areas that have higher or lower risks than their geographical neighbours, while the third proposed model extends this to the spatio-temporal domain to identify clusters/discontinuities and estimate the spatial pattern of disease risk over time. The final piece of work of this thesis allows for discontinuities by using a boundary analysis approach. This approach identifies the boundaries in the spatial risk surface that separate pairs of geographically adjacent areas that exhibit large differences between their risks. Each model is applied to hospital admissions data for respiratory disease from the Greater Glasgow and Clyde Health Board region. Overall, it has been found that the respiratory disease risk surface in Greater Glasgow is not globally spatially smooth. There are numerous pairs of neighbouring areas where a discontinuity in disease risk appears to exist. In addition, the respiratory disease risk in Glasgow appears to increase over time and people living in more deprived areas are at higher risk of respiratory hospital admissions than those living in more affluent areas.

Published
2022
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13. Research on the Level of Synergistic Development of Supply and Demand in China's Health Industry.

Author

Jian, Lingxiang, Yin, Xueqing, and Zhao, Minglou

Abstract

This study combines the latest vague set similarity measurement method with the entropy weight TOPSIS method; furthermore, it applies the grey relational analysis method to establish a model for the synergistic development of supply and demand in the health industry, and it empirically analyzes the development status, synergy level, and synergistic development level of China's health industry supply and demand from 2013 to 2021. The findings reveal that: (1) China's health industry supply maintains steady growth, while demand grows rapidly. However, the overall level of supply and demand development is generally low, indicating significant potential for future development. Moreover, the development of the supply lags behind that of the demand, leading to an increasing gap year by year. (2) The level of synergy between supply and demand in China's health industry is relatively high but is decreasing year by year. Although the degree of synergistic development between supply and demand continues to increase, it remains at a low level, indicating that high coordination between supply and demand comes at the cost of a low development level. (3) There exists a significant regional imbalance in the development of China's health industry, with the overall difference showing a widening trend year by year. Regional disparities are the main source of the overall difference. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Spatio-temporal disease risk estimation using clustering-based adjacency modelling

Author

Yin, Xueqing, Napier, Gary, Anderson, Craig, and Lee, Duncan

Subjects
Statistics and Probability, Spatial Analysis, Health Information Management, Epidemiology, Cluster Analysis, Humans, Bayes Theorem, Computer Simulation, Respiration Disorders
Abstract

Conditional autoregressive models are typically used to capture the spatial autocorrelation present in areal unit disease count data when estimating the spatial pattern in disease risk. This correlation is represented by a binary neighbourhood matrix based on a border sharing specification, which enforces spatial correlation between geographically neighbouring areas. However, enforcing such correlation will mask any discontinuities in the disease risk surface, thus impeding the detection of clusters of areas that exhibit higher or lower risks compared to their neighbours. Here we propose novel methodology to account for these clusters and discontinuities in disease risk via a two-stage modelling approach, which either forces the clusters/discontinuities to be the same for all time periods or allows them to evolve dynamically over time. Stage one constructs a set of candidate neighbourhood matrices to represent a range of possible cluster/discontinuity structures in the data, and stage two estimates an appropriate structure(s) by treating the neighbourhood matrix as an additional parameter to estimate within a Bayesian spatio-temporal disease mapping model. The effectiveness of our novel methodology is evidenced by simulation, before being applied to a new study of respiratory disease risk in Greater Glasgow, Scotland from 2011 to 2017.

Published
2022
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19. sj-pdf-1-smm-10.1177_09622802221084131 - Supplemental material for Spatio-temporal disease risk estimation using clustering-based adjacency modelling

Author

Yin, Xueqing, Napier, Gary, Anderson, Craig, and Lee, Duncan

Subjects
111099 Nursing not elsewhere classified, 111708 Health and Community Services, 160807 Sociological Methodology and Research Methods, FOS: Health sciences, FOS: Sociology
Abstract

Supplemental material, sj-pdf-1-smm-10.1177_09622802221084131 for Spatio-temporal disease risk estimation using clustering-based adjacency modelling by Xueqing Yin, Gary Napier, Craig Anderson and Duncan Lee in Statistical Methods in Medical Research

Published
2022
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20. Effects of continuous intravenous infusion with propofol on intestinal metabolites in rats.

Author

Li, Jiaying, Zhang, Zhongjie, Liu, Hongyu, Qu, Xutong, Yin, Xueqing, Chen, Lu, Guo, Nana, Wang, Changsong, and Zhang, Zhaodi

Subjects
INTRAVENOUS therapy, PROPOFOL, TIME-of-flight mass spectrometers, MICROBIAL metabolites, METABOLITES
Abstract

Microbial metabolites play an important role in regulating intestinal homeostasis and immune responses. Propofol is a common anesthetic in clinic, but it is not clear whether it affects intestinal metabolites in rats. Tail vein puncture was performed after adaptive feeding for 1 month in eight 2-month-old rats and they were given continuous intravenous infusion of propofol for 3 h. The feces of rats were divided into different groups based on time periods, with before and after anesthesia with propofol on days 1, 3 and 7 labeled as groups P, A1, A3 and A7, respectively. The effect of continuous intravenous infusion with propofol on rat fecal metabolites was determined using the non-targeted metabolomics technique gas chromatography coupled with a time-of-flight mass spectrometer analysis. The types and contents of metabolites in rat feces were changed after continuous intravenous infusion with propofol, but the changes were not statistically significant. The contents of the metabolites 3-hydroxyphenylacetic acid and palmitic acid increased from day 3 to 7, and it was shown that the two metabolites were positively correlated at a statistically significant level. Linoleic acid decreased to its lowest level on day 3, and it returned to pre-anesthesia level on day 7. At the same time, linoleic acid metabolism was a metabolic pathway that was co-enriched 7 days after infusion with propofol. Spearman correlation analysis showed that there was significant correlation between some differential metabolites and differential microorganisms. It was observed that zymosterol 1, cytosin and elaidic acid were negatively correlated with Alloprevotella in the A3 vs. P group. In the A7 vs. P group, cortexolone 3 and coprostan-3-one were positively correlated with Faecalibacterium, whilst aconitic acid was negatively correlated with it. In conclusion, the present study revealed statistically insignificant effects of continuous intravenous propofol on the intestinal metabolites in rats. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Risk estimation and discontinuity identification in Bayesian disease mapping

Author

Yin, Xueqing and Yin, Xueqing

Abstract

Disease mapping is the field of epidemiology that estimates the spatial or spatio-temporal pattern in disease risk. Approaches in this field are generally based on data collected on a set of non-overlapping areal units that comprise the study region, and typically utilise counts of the numbers of disease cases within each areal unit. Conditional autoregressive (CAR) models are commonly used to capture the spatial autocorrelation present in areal unit disease count data. The spatial correlation structure that is induced by these models is typically determined by a neighbourhood matrix based on geographical adjacency, which enforces spatial correlation between geographically neighbouring areas and assumes a spatially smooth risk surface. However this may not be realistic in practice, because some pairs of neighbouring areas are likely to exhibit vastly different disease risks. Therefore the aim of this thesis is to develop methodology that allows for discontinuities in the spatial risk pattern when estimating disease risk. The first two models proposed are in a purely spatial setting and account for discontinuities by identifying spatial clusters of areas that have higher or lower risks than their geographical neighbours, while the third proposed model extends this to the spatio-temporal domain to identify clusters/discontinuities and estimate the spatial pattern of disease risk over time. The final piece of work of this thesis allows for discontinuities by using a boundary analysis approach. This approach identifies the boundaries in the spatial risk surface that separate pairs of geographically adjacent areas that exhibit large differences between their risks. Each model is applied to hospital admissions data for respiratory disease from the Greater Glasgow and Clyde Health Board region. Overall, it has been found that the respiratory disease risk surface in Greater Glasgow is not globally spatially smooth. There are numerous pairs of neighbouring areas where a disc

22. Risk estimation and discontinuity identification in Bayesian disease mapping

Author

Yin, Xueqing and Yin, Xueqing

Abstract

Disease mapping is the field of epidemiology that estimates the spatial or spatio-temporal pattern in disease risk. Approaches in this field are generally based on data collected on a set of non-overlapping areal units that comprise the study region, and typically utilise counts of the numbers of disease cases within each areal unit. Conditional autoregressive (CAR) models are commonly used to capture the spatial autocorrelation present in areal unit disease count data. The spatial correlation structure that is induced by these models is typically determined by a neighbourhood matrix based on geographical adjacency, which enforces spatial correlation between geographically neighbouring areas and assumes a spatially smooth risk surface. However this may not be realistic in practice, because some pairs of neighbouring areas are likely to exhibit vastly different disease risks. Therefore the aim of this thesis is to develop methodology that allows for discontinuities in the spatial risk pattern when estimating disease risk. The first two models proposed are in a purely spatial setting and account for discontinuities by identifying spatial clusters of areas that have higher or lower risks than their geographical neighbours, while the third proposed model extends this to the spatio-temporal domain to identify clusters/discontinuities and estimate the spatial pattern of disease risk over time. The final piece of work of this thesis allows for discontinuities by using a boundary analysis approach. This approach identifies the boundaries in the spatial risk surface that separate pairs of geographically adjacent areas that exhibit large differences between their risks. Each model is applied to hospital admissions data for respiratory disease from the Greater Glasgow and Clyde Health Board region. Overall, it has been found that the respiratory disease risk surface in Greater Glasgow is not globally spatially smooth. There are numerous pairs of neighbouring areas where a disc

23. Risk estimation and discontinuity identification in Bayesian disease mapping

Author

Yin, Xueqing and Yin, Xueqing

Abstract

Disease mapping is the field of epidemiology that estimates the spatial or spatio-temporal pattern in disease risk. Approaches in this field are generally based on data collected on a set of non-overlapping areal units that comprise the study region, and typically utilise counts of the numbers of disease cases within each areal unit. Conditional autoregressive (CAR) models are commonly used to capture the spatial autocorrelation present in areal unit disease count data. The spatial correlation structure that is induced by these models is typically determined by a neighbourhood matrix based on geographical adjacency, which enforces spatial correlation between geographically neighbouring areas and assumes a spatially smooth risk surface. However this may not be realistic in practice, because some pairs of neighbouring areas are likely to exhibit vastly different disease risks. Therefore the aim of this thesis is to develop methodology that allows for discontinuities in the spatial risk pattern when estimating disease risk. The first two models proposed are in a purely spatial setting and account for discontinuities by identifying spatial clusters of areas that have higher or lower risks than their geographical neighbours, while the third proposed model extends this to the spatio-temporal domain to identify clusters/discontinuities and estimate the spatial pattern of disease risk over time. The final piece of work of this thesis allows for discontinuities by using a boundary analysis approach. This approach identifies the boundaries in the spatial risk surface that separate pairs of geographically adjacent areas that exhibit large differences between their risks. Each model is applied to hospital admissions data for respiratory disease from the Greater Glasgow and Clyde Health Board region. Overall, it has been found that the respiratory disease risk surface in Greater Glasgow is not globally spatially smooth. There are numerous pairs of neighbouring areas where a disc

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