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深圳市手足口病发病趋势预测：基于气象滞后效应与LSTM模型

Prediction of hand, foot and mouth disease incidence trends in Shenzhen based on meteorological lag effect and LSTM model

    摘要
  • 摘要:
    目的 基于气象滞后效应与长短期记忆网络（LSTM）模型对广东省深圳市手足口病发病趋势进行预测，为建立更科学、精准的气象疾病风险预警系统提供方法学支撑。
    方法 收集中国疾病预防控制系统中2015年1月1日—2024年12月31日深圳市523周的手足口病法定传染病报告卡数据，以周统计≤5岁儿童手足口病病例353 046例，整合周均气温（Temp）、相对湿度（RH）、温湿指数（THI）、体感温度（AT）、风速（WS）等气象参数，通过Spearman秩相关分析筛选显著相关变量构建基础模型（仅病例数）、单因素模型（病例+滞后1～3周Temp/RH）和复合模型（病例+滞后1～3周THI/AT）3种LSTM模型，以均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）评估和比较模型性能。
    结果 测试集对比分析结果显示，引入气象因素的所有模型均优于基础模型（RMSE=211.109，MAE=117.654，R2=0.942）；在单因素模型中，温度滞后2周（Temp-lag2周）模型表现最佳（RMSE=179.915，MAE=108.290，R2=0.958），相对湿度滞后2周（RH-lag2周）模型在所有模型中最优（RMSE=177.457，MAE=97.873，R2=0.959）；在复合模型中，温湿指数滞后2周（THI-lag2周）模型最佳（RMSE=185.061，MAE=103.817，R2=0.956），但仍略逊于RH-lag2周模型；RH-lag2周模型在预测2025年前20周发病趋势时呈双峰结构（第10周与第15周），与2024年春季高峰峰型高度一致，且较2015—2024年平均高峰期（第9～35周）整体显著前移。
    结论 基于滞后2周RH构建的LSTM模型可有效表征湿热环境对肠道病毒存活与传播的促进作用，显著提升手足口病发病趋势预测精度。

     

    Abstract:
    Objective To predict the incidence trends of hand, foot and mouth disease (HFMD) in Shenzhen city, Guangdong province based on the meteorological lag effect and the long short-term memory (LSTM) model, thereby providing methodological support for establishing a more scientific and accurate meteorological disease risk early warning system.
    Methods Data from 523 weeks (January 1, 2015 to December 31, 2024) of HFMD case reports in the China Disease Control and Prevention Information System were collected. A total of 353 046 HFMD cases in children aged ≤5 years were aggregated weekly. Meteorological parameters, including average weekly temperature (Temp), relative humidity (RH), temperature-humidity index (THI), apparent temperature (AT), and wind speed (WS), were integrated. Significant correlated variables were screened by Spearman rank correlation analysis to construct three types of LSTM models: a baseline model (case count only), single-factor models (case count + Temp/RH lagged 1–3 weeks), and composite models (case count + THI/AT lagged 1–3 weeks). Model performance was evaluated and compared via the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2).
    Results Comparative analysis on the test set showed that all the models incorporating meteorological factors outperformed the baseline model (RMSE = 211.109, MAE = 117.654, R2 = 0.942). Among the single-factor models, the model with Temp lagged by 2 weeks (Temp-lag2) performed best (RMSE = 179.915, MAE = 108.290, R2 = 0.958), and the model with RH lagged by 2 weeks (RH-lag2) was optimal among all the models (RMSE = 177.457, MAE = 97.873, R2 = 0.959). Among the composite models, the model with THI lagged by 2 weeks (THI-lag2) performed best (RMSE = 185.061, MAE = 103.817, R2 = 0.956) but was slightly inferior to the RH-lag2 model. The RH-lag2 model predicted a bimodal pattern (peaks at weeks 10 and 15) for the HFMD incidence trends in the first 20 weeks of 2025, which closely matched the spring peak pattern of 2024 and showed a significant overall shift forward compared to the average peak period (weeks 9–35) from 2015 to 2024.
    Conclusions The LSTM model constructed based on RH lagged by 2 weeks effectively characterizes the promoting effects of a hot and humid environment on the survival and transmission of enteroviruses, significantly improving the prediction accuracy of HFMD incidence trends.

     

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