Abstract

Objective

We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients’ claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

Materials and Methods

We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties.

Results

STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model.

Conclusions

By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization.

中文翻译：

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STAN：使用真实世界证据进行流行病预测的时空注意力网络

摘要

客观的

我们的目标是开发一种混合模型，通过以下方式更早、更准确地预测大流行中的感染病例数：(1) 使用来自不同县和州的患者索赔数据，了解当地疾病状况和医疗资源利用率；(2) 利用地点之间的人口统计相似性和地理邻近性；(3) 将流行病传播动力学整合到深度学习模型中。

材料和方法

我们提出了一个用于大流行预测的时空注意力网络（STAN）。它使用图注意力网络来捕获疾病动态的时空趋势，并预测未来固定天数的病例数。我们还设计了一个基于动态的损失项来增强长期预测。STAN 使用真实世界的患者索赔数据和美国各县随时间推移的 COVID-19 统计数据进行了测试。

结果

STAN 在长期和短期预测方面均优于传统流行病学模型，例如易感者-感染者-恢复 (SIR)、易感者-暴露-感染者-恢复 (SEIR) 和深度学习模型，实现平均值降低高达 87%与最佳基线预测模型相比的平方误差。

结论

通过结合现实世界的索赔数据和疾病病例计数数据的信息，STAN 可以更好地预测疾病状态和医疗资源利用率。