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使用统计数学和深度学习模型,对印度急性腹和登革热的传播进行比较估计

Avaneesh Singh¹, Krishna Kumar Sharma², Kailash Wamanrao Kalare³

¹Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology Gorakhpur, Gorakhpur, Uttar Pradesh, 273010, India.

Scientific reports

|October 6, 2025

概括

在印度预测急性腹和登革热需要选择合适的模型. 阿里马模型最好预测腹,而Seq2Seq则在登革热方面表现出色,改善了公共卫生资源的分配.

科学领域:

流行病学流行病学
公共卫生公共卫生
数据科学数据科学数据科学

背景情况:

预测急性腹和登革热等传染病爆发对公共卫生至关重要.
印度面临着严重的疾病负担,需要准确的资源管理预测模型.

研究的目的:

为了比较统计,数学和深度学习模型的预测准确度,用于预测印度的急性腹和登革热.
确定最有效的疾病传播预测模型,以告知公共卫生战略.

主要方法:

对10个时间序列预测模型的比较分析:ARIMA,回归,贝叶斯线性回归与多输出回归器+XGBoost,SIR模型,Prophet,N-BEATS,GluonTS,LSTM和Seq2Seq.
利用从2011年1月1日到2024年33周的每周病例和死亡数据.
使用平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 评估模型性能.

主要成果:

在预测急性腹病例方面,ARIMA模型表现出卓越的性能 (RMSE: 317.7,MAPE: 2.4).
在登革热病例预测方面,Seq2Seq模型取得了最好的结果 (RMSE: 399.1,MAPE: 6.3).
像N-BEATS和LSTM这样的深度学习模型显示出强大的预测能力,而传统模型的错误率更高.

结论:

模型选择对于准确的疾病预测和有效的公共卫生干预至关重要.
调查结果为决策者提供了洞察力,以优化医疗保健资源分配和实施有针对性的疾病控制战略.

关键词:

在阿里马,阿里马就是阿里马.深度学习是一种深度学习.疾病预测疾病预测这是LSTM的LSTM.这是一个Seq2Seq模型.统计模型统计模型

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