用新的混合分解方法和异常检测分析预测COVID-19病例的气象和人类活动数据：巴西首都的个例研究
Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: a case study in the capitals of Brazil
摘要：
2020年，巴西是拉丁美洲COVID-19病例最多的国家，首都城市受疫情影响最严重。巴西的气候因国家的领土延伸、地形、地理和其他因素而异。由于最常见的COVID-19症状与呼吸系统有关，许多研究人员研究了COVID-19病例数量与温度，湿度，降雨量等气象变量之间的相关性。此外，由于其高传播率，一些研究人员分析了人类流动性对COVID-19传播动态的影响。在预测COVID-19病例的传播时，缺乏考虑这两个变量的文献。本文分析了COVID-19病例数量与人口流动性之间的相关性，以及巴西首都的气象数据。我们发现，这些变量之间的相关性取决于城市所在的地区。我们使用与COVID-19病例显著相关的变量来预测巴西所有首都的COVID-19感染人数，并提出了一种结合Ensemble Empirical Mode Decomposition（EEMD）方法和自回归积分移动平均外源输入（ARIMAX）方法的预测方法，我们称之为EEMD-ARIMAX。在分析结果后，使用基于信号处理的异常检测方法进一步研究了不良预测。计算测试表明，EEMD-ARIMAX实现了预测比ARIMAX高26.73%。此外，平均均方根误差（RMSE）的30.69%的改善时，注意到应用EEMD-ARIMAX方法的异常检测后归一化的数据。
英文摘要：
In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
