2014年至2019年气象因素对宁波手足口病发病率的影响：因果卷积神经网络

Impact of meteorological factors on the incidence of hand, foot and mouth disease in Ningbo from 2014 to 2019: a causal convolutional neural networks.

作者信息

Du Bingqian, Ren Zhiqiang, Song Ziyu, Yuan Min, Li Zhenjun

机构信息

National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102211, China.

School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China.

出版信息

BMC Public Health. 2025 Jul 11;25(1):2434. doi: 10.1186/s12889-025-23634-y.

DOI:10.1186/s12889-025-23634-y

PMID:40646519

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12247356/

Abstract

BACKGROUND

Hand, foot, and mouth disease (HFMD) is recognized as a climate-sensitive disease, yet the precise influence of meteorological factors on its incidence remains underexplored. This study leverages Causal Convolutional Neural Networks (Causal CNNs) to investigate the epidemiological characteristics of HFMD in Ningbo City, China, from 2014 to 2019, and to assess the predictive role of meteorological factors, offering novel insights for real-time surveillance and control.

METHODS

Daily meteorological data and HFMD incidence data for Ningbo from 2014 to 2019 were obtained from the Chinese Center for Disease Control and Prevention. The Causal CNNs and the Granger causality test were applied for prediction and analysis.

RESULTS

From 2014 to 2019, the average annual incidence of HFMD in Ningbo was 398.66 per 100,000. The disease showed notable seasonality and annual periodicity, with a bimodal distribution peaking in June-July and October-November each year. The daily mean temperature and relative humidity demonstrated similar annual cyclical variations to HFMD incidence, while daily mean pressure exhibited opposite trends. The Causal CNNs model indicated that daily mean temperature, relative humidity, pressure, and wind speed had better predictive effects with a lag of 19 days [the mean square errors (MSE) were 0.490, 0.333, 0.529, 0.325, respectively, and the mean absolute errors (MAE) were 0.491, 0.355, 0.531, 0.433, respectively]. The Granger causality test confirmed significant correlations between HFMD incidence and daily mean temperature, relative humidity, pressure, and wind speed (The F values were 5.660, 6.878, 4.330, 1.726, respectively, and all P < 0.05).

CONCLUSION

Meteorological factors, particularly mean temperature, relative humidity, pressure, and wind speed, may significantly influence HFMD incidence in Ningbo. The Causal CNNs model provides relatively accurate predictions, supporting its potential for enhancing HFMD surveillance and informing targeted public health interventions.

摘要

背景

手足口病（HFMD）被认为是一种对气候敏感的疾病，然而气象因素对其发病率的确切影响仍未得到充分探索。本研究利用因果卷积神经网络（Causal CNNs）调查了2014年至2019年中国宁波市手足口病的流行病学特征，并评估气象因素的预测作用，为实时监测和防控提供新见解。

方法

从中国疾病预防控制中心获取了2014年至2019年宁波市的每日气象数据和手足口病发病率数据。应用因果卷积神经网络和格兰杰因果检验进行预测和分析。

结果

2014年至2019年，宁波市手足口病的年均发病率为每10万人398.66例。该疾病呈现出显著的季节性和年度周期性，呈双峰分布，每年6-7月和10-11月达到峰值。日平均温度和相对湿度与手足口病发病率呈现出相似的年度周期性变化，而日平均气压则呈现相反趋势。因果卷积神经网络模型表明，日平均温度、相对湿度、气压和风速在滞后19天时具有更好的预测效果[均方误差（MSE）分别为0.490、0.333、0.529、0.325，平均绝对误差（MAE）分别为0.491、0.355、0.531、0.433]。格兰杰因果检验证实手足口病发病率与日平均温度、相对湿度、气压和风速之间存在显著相关性（F值分别为5.660、6.878、4.330、1,726，且所有P< 0.05）。