Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil.
Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista "Júlio Mesquita Filho", Rodovia Araraquara-Jaú, Km 1, Campus Ville, Araraquara 14800-903, Brazil.
Int J Environ Res Public Health. 2021 Nov 4;18(21):11595. doi: 10.3390/ijerph182111595.
In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (R0, Re) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.
在本文中，我们研究了假期和社区流动性对巴西 COVID-19 传播率和死亡人数的影响。我们确定了全国性节日和标志性节日，以评估它们对确诊病例和死亡人数报告的影响。首先，我们使用一个单变量模型，将感染人数作为输入数据，来预测死亡人数。这个简单的模型与一个更强大的深度学习多变量模型进行了比较，该模型使用 SEIRD 模型的流动性和传播率（R0、Re）作为输入数据。通过主成分分析（PCA）方法生成社区流动性的主成分模型，为多变量模型添加了改进的输入特征。深度学习模型架构是一个 LSTM 堆叠层，结合密集层，对 COVID-19 导致的每日死亡人数进行回归。与标准的单变量数据驱动模型相比，增量式多变量模型可以将预测性能提高高达 18.99%。
