一种预测罗马尼亚 COVID-19 演变的自监督神经-分析方法
A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania
AI总结：
提出自监督卷积网络结合随机坐标下降推断 Modified-SEIR 参数的方法，仅用罗马尼亚每日死亡数据即可预测疫情演变，病死率约0.3%，未来三周死亡预测准确且优于网格搜索。
摘要：
分析并理解 COVID-19 大流行的传播和演变，对于制定最佳社会与医疗政策、预见其后果以及应对随后出现的所有社会经济影响而言是必不可少的。我们从计算和机器学习的角度来处理这一重要问题。更具体地说，我们希望基于罗马尼亚患者，从统计上估计新型冠状病毒 COVID-19 的所有相关参数，例如再生数、病死率或传染期长度，并能够预测未来结果。这项工作十分重要，因为众所周知，这些因素在全球各地存在差异，并且可能取决于许多原因，包括社会、医疗、年龄和遗传因素。我们使用最近发表的 SEIR 改进版本；SEIR 是传染病领域经典且成熟的模型。我们希望仅依据唯一可靠、真实的测量数据，即死亡人数，来推断支配罗马尼亚大流行演变的模型全部参数。一旦估计出模型参数，我们就能够预测所有其他相关指标，例如暴露者和感染者人数。为此，我们提出一种自监督方法，用于训练深度卷积网络，使其在给定观测到的每日死亡人数的情况下，猜出正确的 Modified-SEIR 模型参数集合。随后，我们使用随机坐标下降方法对该解进行精化。我们将深度学习优化方案与经典网格搜索方法进行比较，并表明其在计算时间和预测准确性方面均有显著提升。我们发现罗马尼亚的病死率结果较为乐观，可能约为 0.3%；同时我们还证明，该模型能够正确预测未来长达三周的每日死亡人数。
英文摘要：
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.
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