The present work raises an investigation about prediction and the feature importance to estimate the COVID-19 infection, using Machine Learning approach. Our work analyzed the inclusion of climatic features, mobility, government actions and the number of cases per health sub-territory from an existing model. The Random Forest with Permutation Importance method was used to assess the importance and list the thirty most relevant that represent the probability of infection of the disease. Among all features, the most important were: i) the variables per region health stand out, ii) period comprised between the date of notification and symptom onset, iii) symptoms features as fever, cough and sore throat, iv) variables of the traffic flow and mobility, and also v) wheathers features. The model was validated and reached an accuracy average of 81.82%, whereas the sensitivity and specificity achieved 87.52% and the 78.67% respectively in the infection estimate. Therefore, the proposed investigation represents an alternative to guide authorities in understanding aspects related to the disease.

目前的工作对使用机器学习方法估计 COVID-19 感染的预测和特征重要性进行了调查。我们的工作分析了现有模型中包含的气候特征、流动性、政府行动和每个卫生亚领土的病例数。具有排列重要性的随机森林方法用于评估重要性并列出代表疾病感染概率的三十个最相关的。在所有特征中，最重要的是：i）每个地区健康的变量，ii）从通知日期到症状出现之间的时间段，iii）发烧、咳嗽和喉咙痛等症状特征，iv）交通变量流动性和流动性，以及 v) 惠瑟的特征。该模型经过验证，平均准确率达到 81.82%，而在感染估计中的敏感性和特异性分别达到了87.52%和78.67%。因此，拟议的调查代表了指导当局了解与该疾病相关的方面的替代方案。