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首页> 外文期刊>The American Journal of Tropical Medicine and Hygiene >A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China
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A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China

机译：一种新的混合模型，采用自回归综合移动平均线和恒县结核病发病率的通用回归神经网络

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摘要

It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.
机译：由于大型短暂性群体，人类免疫缺陷病毒/结核币和潜在感染，致力于在恒县完全消除结核病的艰巨任务。因此，高精度预测模型可用于预防和控制结核病。在本研究中，四种模型，包括基本的自回归综合移动平均（ARIMA）模型，传统的ARIMA广义回归神经网络（GRNN）模型，基本的GRNN模型和新的ARIMA-GRNN混合模型用于适应和预测结核病的发生率。包括平均绝对误差（MAE）的参数，平均绝对百分比误差（MAPE）和均方误差（MSE）用于评估这些模型的性能，以拟合历史和预期数据。当应用于历史数据时，新的ARIMA-GRNN模型相对于传统的ARIMA-GRNN模型和基本ARIMA模型具有优异的适应性，并且当用作在随后的6个月内预测入射的预测模型时。我们的研究结果表明，新的Arima-Grnn模型可能更适合预测恒县结核病的发病率而不是传统模式。

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  • 作者

    Wei Wudi; Jiang Junjun; Gao Lian; Liang Bingyu; Huang Jiegang; Zang Ning; Ning Chuanyi; Liao Yanyan; Lai Jingzhen; Yu Jun; Qin Fengxiang; Chen Hui; Su Jinming; Ye Li; Liang Hao;

  • 作者单位

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Heng Cty Ctr Dis Control &

    Prevent Dept Infect Dis 16 Gongyuan Rd Nanning Heng County Peoples;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Affiliated Hosp 1 Geriatr Digest Dept Internal Med 6 Shuangyong Rd Nanning;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

    Guangxi Med Univ Guangxi Key Lab AIDS Prevent &

    Treatment 22 Shuangyong Rd Nanning Guangxi;

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  • 正文语种 eng
  • 中图分类 地方病学;
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