Ballı Serkan
Department of Information Systems Engineering, Faculty of Technology, Muğla Sıtkı Koçman University, 48000, Muğla, Turkey.
Chaos Solitons Fractals. 2021 Jan;142:110512. doi: 10.1016/j.chaos.2020.110512. Epub 2020 Nov 28.
The Covid-19 pandemic is the most important health disaster that has surrounded the world for the past eight months. There is no clear date yet on when it will end. As of 18 September 2020, more than 31 million people have been infected worldwide. Predicting the Covid-19 trend has become a challenging issue. In this study, data of COVID-19 between 20/01/2020 and 18/09/2020 for USA, Germany and the global was obtained from World Health Organization. Dataset consist of weekly confirmed cases and weekly cumulative confirmed cases for 35 weeks. Then the distribution of the data was examined using the most up-to-date Covid-19 weekly case data and its parameters were obtained according to the statistical distributions. Furthermore, time series prediction model using machine learning was proposed to obtain the curve of disease and forecast the epidemic tendency. Linear regression, multi-layer perceptron, random forest and support vector machines (SVM) machine learning methods were used. The performances of the methods were compared according to the RMSE, APE, MAPE metrics and it was seen that SVM achieved the best trend. According to estimates, the global pandemic will peak at the end of January 2021 and estimated approximately 80 million people will be cumulatively infected.
新冠疫情是过去八个月来席卷全球的最重要的健康灾难。目前尚无明确的结束日期。截至2020年9月18日，全球已有超过3100万人感染。预测新冠疫情趋势已成为一个具有挑战性的问题。在本研究中，美国、德国以及全球2020年1月20日至2020年9月18日的新冠疫情数据来自世界卫生组织。数据集包含35周的每周确诊病例和每周累计确诊病例。然后使用最新的新冠疫情每周病例数据检查数据分布，并根据统计分布获取其参数。此外，还提出了使用机器学习的时间序列预测模型来获取疾病曲线并预测疫情趋势。使用了线性回归、多层感知器、随机森林和支持向量机（SVM）等机器学习方法。根据均方根误差（RMSE）、平均绝对误差（APE）、平均绝对百分比误差（MAPE）指标对这些方法的性能进行了比较，结果发现支持向量机取得了最佳趋势。据估计，全球疫情将在2021年1月底达到峰值，累计感染人数预计约为8000万。
