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

机译:多变量时间序列分析对我国B级通知疾病与国内生产总值(GDP)的动态关系

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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.
机译:传染病监测对于疾病控制和预防具有重要意义,并应对中国的B级通知疾病支付更多的关注。同时,根据国际货币基金组织(IMF),中国国内生产总值(GDP)的年增长率将在多年飙升后减速7%以下。在这种情况下,如果中国GDP增长在未来五年中,如果中国GDP增长持续低于7%,则会回答中国传染病发病率将发生的情况。首先,提出了时间图和互相关矩阵以说明数据的特征。然后,提出了多变量时间序列(MTS)模型来探讨发射率和GDP之间的动态关系。在本研究中考虑了三种MTS模型,即原始系列的矢量自动回归(VAR)模型,用于差异系列和纠错模型(ECM)的VAR模型。纠正术语的等级被视为模型选择的指标。最后,我们的结果表明,在中国可能需要四种传染病(流行性出血热,百日咳,猩红热和梅毒),因为它们自2010年自2010年以来的发病率增加。

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