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可解释的空间认同基于神经网络的流行病预测

Lanjun Luo¹, Boxiao Li², Xueyan Wang³

¹School of Management, North Sichuan Medical College, Nanchong, China.

Scientific reports

|October 24, 2023

概括

本研究引入了一个可解释空间身份 (ISID) 神经网络,用于传染病预测. ISID模型为公共卫生应用提供了准确的预测,并增强了可解释性.

科学领域:

流行病学流行病学
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

传染病预测对于公共卫生管理至关重要.
目前用于流行病预测的深度学习模型通常是复杂的,缺乏可解释性.
现有的方法难以平衡预测准确性和清晰的解释.

研究的目的:

开发一个可解释和轻量级的神经网络,用于区域每周传染病人数预测.
通过模型可解释性,增强对流行病传播动态的理解.
为公共卫生专家提供流行病风险分析的可靠工具.

主要方法:

将经典的时空身份模型 (STID) 简化为可解释空间身份 (ISID) 网络.
纳入一个可选的空间身份矩阵来建模区域间传染.
使用夏普利添加式解释 (SHAP) 方法进行后期模型解释.

主要成果:

与现有方法相比,ISID模型显示出令人满意的流行病预测性能.
SHAP分析显示,ISID在输入序列中优先考虑近距离和远距离的数据点.
该模型有效地学习了不同地区之间的传染关系.

结论:

ISID神经网络为传染病人数预测提供了可靠和可解释的解决方案.
该模型的可解释性有助于公共卫生专家了解流行病的动态.
这种方法为时空流行病风险分析提供了更为连贯的框架.

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