[论文解读] A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania

Stochiţoiu Rd, Marian Petrică+3 more|arXiv (Cornell University)|Jun 23, 2020

COVID-19 epidemiological studies参考文献 15被引用 5

一句话总结

本文提出一种自监督深度学习方法，仅使用每日死亡人数估计罗马尼亚的修正SEIR模型参数，从而实现对COVID-19传播趋势的准确预测。通过将卷积神经网络与随机坐标下降法结合，该方法显著减少了计算时间，并在预测精度上优于网格搜索方法，成功实现对死亡人数长达三周的预测，病例死亡率估计约为0.3%。

ABSTRACT

Analysing and understanding the transmission and evolution of the COVID-19 pandemic is mandatory to be able to design the best social and medical policies, foresee their outcomes and deal with all the subsequent socio-economic effects. We address this important problem from a computational and machine learning perspective. More specifically, we want to statistically estimate all the relevant parameters for the new coronavirus COVID-19, such as the reproduction number, fatality rate or length of infectiousness period, based on Romanian patients, as well as be able to predict future outcomes. This endeavor is important, since it is well known that these factors vary across the globe, and might be dependent on many causes, including social, medical, age and genetic factors. We use a recently published improved version of SEIR, which is the classic, established model for infectious diseases. We want to infer all the parameters of the model, which govern the evolution of the pandemic in Romania, based on the only reliable, true measurement, which is the number of deaths. Once the model parameters are estimated, we are able to predict all the other relevant measures, such as the number of exposed and infectious people. To this end, we propose a self-supervised approach to train a deep convolutional network to guess the correct set of Modified-SEIR model parameters, given the observed number of daily fatalities. Then, we refine the solution with a stochastic coordinate descent approach. We compare our deep learning optimization scheme with the classic grid search approach and show great improvement in both computational time and prediction accuracy. We find an optimistic result in the case fatality rate for Romania which may be around 0.3% and we also demonstrate that our model is able to correctly predict the number of daily fatalities for up to three weeks in the future.

研究动机与目标

• 仅基于每日死亡人数，估计罗马尼亚COVID-19疫情的关键流行病学参数，如基本再生数、死亡率和传染期。
• 开发一种计算高效的替代方法，用于修正SEIR模型中的参数估计，替代传统网格搜索。
• 实现在罗马尼亚范围内对每日死亡人数、潜伏者和感染者数量的准确短期预测。
• 通过将参数校准至本地数据，考虑年龄、遗传因素和医疗基础设施等国家特异性因素。

提出的方法

• 使用自监督方式训练深度卷积神经网络，从每日死亡人数序列中预测修正SEIR模型的正确参数集。
• 模型使用预训练网络初始化，该网络学习将死亡人数模式映射到合理参数集，无需显式标签。
• 采用随机坐标下降算法优化神经网络的预测结果，以提高精度和收敛速度。
• 该方法在计算效率和预测性能方面均优于经典网格搜索方法。
• 使用修正SEIR模型模拟疾病传播过程，所有参数均仅从死亡数据中推断。
• 最终模型输出未来长达三周的每日死亡人数、感染者和潜伏者数量的预测结果。

实验结果

研究问题

• RQ1 自监督深度学习模型能否仅基于罗马尼亚的每日死亡人数准确推断修正SEIR模型参数？
• RQ2 与传统网格搜索相比，所提出的深度学习方法在计算速度和预测精度方面表现如何？
• RQ3 基于模型参数推断，罗马尼亚的病例死亡率估计值是多少？
• RQ4 该模型能否可靠地预测未来三周内的每日死亡人数？
• RQ5 该模型在预测其他流行病学指标（如感染者和潜伏者数量）方面具有多大程度的泛化能力？

主要发现

• 该模型能够以高精度提前三周预测罗马尼亚的每日死亡人数。
• 罗马尼亚的病例死亡率估计值约为0.3%，表明其疫情预后相对乐观，优于其他国家。
• 自监督深度学习方法相比标准网格搜索方法显著减少了计算时间。
• 随机坐标下降的集成有效提升了模型的参数估计精度和收敛速度。
• 该方法可仅基于死亡数据可靠推断其他流行病学指标（如潜伏者和感染者数量）。
• 与经典网格搜索优化方法相比，该方法在速度和精度方面均表现出更优性能。