The COVID-19 pandemic continues to have a destructive effect on the health and well-being of the global population. A vital step in the battle against it is the successful screening of infected patients, together with one of the effective screening methods being radiology examination using chest radiography. Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. The study’s primary motivation is to be able to estimate with a certain level of accuracy the number of deaths due to COVID-19, managing to model the progression of the pandemic. Predicting the number of possible deaths from COVID-19 can provide governments and decision-makers with indicators for purchasing respirators and pandemic prevention policies. Thus, this work presents itself as an essential contribution to combating the pandemic. Kalman Filter is a widely used method for tracking and navigation and filtering and time series. Designing and tuning machine learning methods are a labor- and time-intensive task that requires extensive experience. The field of automated machine learning Auto Machine Learning relies on automating this task. Auto Machine Learning tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. This paper presents an objective method of forecasting the COVID-19 outbreak using Kalman Filter and Auto Machine Learning. We use a COVID-19 dataset of Ceará, one of the 27 federative units in Brazil. Ceará has more than 235,222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The TPOT automobile model showed the best result with a 0.99 of \(R^2\) score.

COVID-19 大流行继续对全球人口的健康和福祉产生破坏性影响。抗击疫情的重要一步是成功筛查感染患者，其中有效的筛查方法之一是使用胸部X光检查进行放射学检查。认识跨时间和社会因素的流行病增长模式可以提高我们创建流行病传播设计的能力，包括预测最终爆发发病率或死亡率影响的估计强度的关键工作。该研究的主要动机是能够以一定的准确度估计 COVID-19 造成的死亡人数，从而模拟大流行的进展。预测 COVID-19 可能导致的死亡人数可以为政府和决策者提供购买呼吸器和流行病预防政策的指标。因此，这项工作对抗击这一流行病做出了重要贡献。卡尔曼滤波器是一种广泛使用的跟踪、导航、滤波和时间序列方法。设计和调整机器学习方法是一项劳动密集型和时间密集型的任务，需要丰富的经验。自动化机器学习领域自动机器学习依赖于自动化此任务。自动机器学习工具使新手用户能够创建有用的机器学习单元，而专家可以使用它们来腾出宝贵的时间来执行其他任务。本文提出了一种使用卡尔曼滤波器和自动机器学习来预测 COVID-19 爆发的客观方法。我们使用巴西 27 个联邦单位之一塞阿拉 (Ceará) 的 COVID-19 数据集。塞阿拉州有超过 235,222 例确诊的 COVID-19 病例，8850 人因此病死亡。TPOT汽车模型表现最好，得分为0.99\(R^2\)分数。