Bayesian spatio-temporal random coefficient time series (BaST-RCTS) model of infectious disease

Zhang, Tao; Zhang, Xingyu; Ma, Yue; Zhou, Xiao‐Hua; Liu, Yuanyuan; Zhu, Feng; Li, Xiaosong

doi:10.1016/j.mbs.2014.09.015

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…At least two kinds of information were proved useful by previous studies 28,29 : historical incidence data and exogenous variables including but not limited to GDP. Therefore, this study built MTS models to account for both of them.…”

Section: Discussionmentioning

confidence: 99%

Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China

Zhang

Yin

Zhou

et al. 2016

Sci Rep

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The surveillance of infectious diseases is of great importance for disease control and prevention, and more attention should be paid to the Class B notifiable diseases in China. Meanwhile, according to the International Monetary Fund (IMF), the annual growth of Chinese gross domestic product (GDP) would decelerate below 7% after many years of soaring. Under such circumstances, this study aimed to answer what will happen to the incidence rates of infectious diseases in China if Chinese GDP growth remained below 7% in the next five years. Firstly, time plots and cross-correlation matrices were presented to illustrate the characteristics of data. Then, the multivariate time series (MTS) models were proposed to explore the dynamic relationship between incidence rates and GDP. Three kinds of MTS models, i.e., vector auto-regressive (VAR) model for original series, VAR model for differenced series and error-correction model (ECM), were considered in this study. The rank of error-correction term was taken as an indicator for model selection. Finally, our results suggested that four kinds of infectious diseases (epidemic hemorrhagic fever, pertussis, scarlet fever and syphilis) might need attention in China because their incidence rates have increased since the year 2010.

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Section: Discussionmentioning

confidence: 99%

Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China

Zhang

Yin

Zhou

et al. 2016

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The multiple regression results only depends on univariate screening may be dramatically biased and misleading. Moreover, from a methodological perspective, the spatio-temporal model itself has the property of “borrowing strength” [ 42 ]. Our data contained both temporal and spatial information, and neglecting spatial information will lead to deviations.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province

Liao

Wang

et al. 2022

BMC Public Health

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Object Scarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control. Method This research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever. Results From 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1–2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities. Conclusion Scarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.

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Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Haque,

Mengersen,

Barr

et al. 2024

Environmental Research

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Bayesian spatio-temporal random coefficient time series (BaST-RCTS) model of infectious disease

Cited by 6 publications

References 33 publications

Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China

Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China

Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

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