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2017 年至 2022 年中国山西省手足口病的流行病学特征、空间聚集性及月度发病预测。

Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.

机构信息

School of Public Health, Shanxi Medical University, Taiyuan, China.

Shanxi Center for Disease Control and Prevention, Taiyuan, China.

出版信息

Epidemiol Infect. 2023 Mar 14;151:e54. doi: 10.1017/S0950268823000389.

DOI:10.1017/S0950268823000389
PMID:37039461
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10126901/
Abstract

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 838.25, 2.25 3.08, and 461.96 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics.

摘要

手足口病(HFMD)是一种常见的世界性传染病,其流行会导致沉重的疾病负担。在过去的十年中,手足口病在中国儿童中广泛传播,山西省是北方受灾严重的省份。山西省地处温带季风气候区,国内生产总值超过 2.5 万亿元。了解气象和经济背景与中国北方相似地区的手足口病基本特征非常重要。我们旨在调查手足口病的流行病学特征,识别空间聚集性并预测手足口病的月度发病率。所有报告的手足口病病例均来自山西省疾病预防控制中心。总体手足口病发病率显示出从 2017 年到 2020 年的显著下降趋势,在 2021 年再次上升。受影响的主要是年龄<5 岁的儿童,男性患者的手足口病发病率较高(相对风险:1.316)。分布呈季节性趋势,主要高峰在 6 月和 7 月,除 2020 年外,次要高峰在 10 月和 11 月。其他肠道病毒是大多数年份手足口病的主要病原体。手足口病病例较多的地区主要在山西省中部,2017 年和 2018 年的空间聚类显示出阳性的全局空间相关性。局部空间自相关分析显示,2018 年热点和次热点集中在晋中和平阳。基于 2021 年 9 月至 2022 年 8 月的月度发病率,长短期记忆(LSTM)和季节性自回归综合移动平均(SARIMA)模型的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)分别为 386.58、838.25、2.25、3.08 和 461.96、963.13,表明 LSTM 的预测精度优于 SARIMA。LSTM 模型可用于预测手足口病的月度发病率,为手足口病流行提供早期预警。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/bf2291d1b70b/S0950268823000389_fig9.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/c20aa2dab4d9/S0950268823000389_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/6e56d132c1eb/S0950268823000389_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/bf2291d1b70b/S0950268823000389_fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/07aa4790ae95/S0950268823000389_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/fc78d70c8caa/S0950268823000389_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/ce783c0f3c7f/S0950268823000389_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/0e9bd98d1c13/S0950268823000389_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/9bcc1d732b6c/S0950268823000389_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/53bcac0ef49c/S0950268823000389_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/c20aa2dab4d9/S0950268823000389_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/6e56d132c1eb/S0950268823000389_fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/034c/10126901/bf2291d1b70b/S0950268823000389_fig9.jpg

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