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Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models

机译:使用灰色和砂马型模型的伤寒和副伤寒累积累积分析的时间序列分析

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Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as x ^ ( t ) = ? 14.98 ( e ? 0.10 ( t ? 2004 ) ? e ? 0.10 ( t ? 2005 ) ) and x ^ ( t ) = ? 4.96 ( e ? 0.19 ( t ? 2004 ) ? e ? 0.19 ( t ? 2005 ) ) respectively. SARIMA (0,1,7) × (1,0,1) 12 was selected among a collection of constructed models for high R 2 and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.
机译:伤寒和竺曲面的Freves是常见的肠道疾病,导致中国残疾和死亡。在中国2004年至2016年间伤寒和副伤害的发病资料进行了描述,探讨了年龄特异性和地理分布等流行病学特征。两种F FEVERS的累积发病率显示出全国性的重大减少,2016年表现为伤寒的73.9%,对2004年的副伤害86.6%。累计发病率在所有年龄次组中都落下,0-4岁儿童是最易感的最近几年。三个西南省(云南,贵州和广西)是顶级高兴处区域。灰色模型GM(1,1)和季节性自回归综合移动平均线(Sarima)模型用于提取疾病的长期趋势。通过GM(1,1)为x ^(t)= x ^(t)=? 14.98(e?0.10(t?2004)?e?0.10(t?2005))和x ^(t)=? 4.96(e?0.19(t?2004)?e?0.19(t-2005)))。 Sarima(0,1,7)×(1,0,1)12是在构造模型的集合中选择的高R 2和低误差。两个FEVERS的预测模型预测累积的趋势,以继续略微下降趋势,并在不久的几年中保持周期性季节性。这种数据驱动的洞察力对于预防和控制伤寒和副伤难作为严重传染病的信息和可操作的信息。

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