Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group—deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.

中文翻译：

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使用多层感知器对 COVID-19 感染的传播进行建模。


冠状病毒（COVID-19）是一种高度传染性疾病，已引起全世界公众的关注。此类疾病的建模对于预测其影响极其重要。虽然经典的统计建模可以提供令人满意的模型，但它也可能无法理解数据中包含的复杂性。在本文中，作者使用了一个公开的数据集，其中包含 51 天内（2020 年 1 月 22 日至 3 月 12 日）406 个地点的感染者、康复者和死亡患者的信息。该数据集旨在成为时间序列数据集，被转换为回归数据集并用于训练多层感知器 (MLP) 人工神经网络 (ANN)。训练的目的是获得每个时间单位内所有地点的最大患者数量的全球模型。使用网格搜索算法改变 MLP 的超参数，总共有 5376 种超参数组合。使用这些组合，总共训练了 48384 个 ANN（每个患者组 16128 个 - 死亡、康复和感染），并且使用确定系数 ( ）。使用 5 倍的 K 倍算法进行交叉验证。实现的最佳模型由 4 个隐藏层组成，每层有 4 个神经元，并使用 ReLU 激活函数，确诊患者模型得分为 0.98599，死亡模型得分为 0.99429，康复患者模型得分为 0.97941。当进行交叉验证时，确诊患者模型的这些分数降至 0.94，康复患者模型的分数降至 0.781，死亡患者模型的分数降至 0.986，显示死亡患者模型的稳健性较高，确诊患者模型的稳健性良好，而康复患者模型的稳健性较低。