Fok Ying Tung Graduate School, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
Biomed Res Int. 2022 Apr 25;2022:2601149. doi: 10.1155/2022/2601149. eCollection 2022.
The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.
新冠疫情是一场全球性危机，影响了数十亿人，并造成了无数的经济损失。为了应对这场危机，人们提出了不同的方法，包括医疗措施和技术创新，例如人工智能技术来诊断和预测新冠病例。虽然人们对美国和中国给予了很多关注，但对欠发达国家，如印度，关注较少。在这项研究中，我对印度的新冠疫情进行了分析，使用了来自不同来源的数据集。我构建了几个机器学习模型来预测新冠的传播，使用了不同组合的输入特征，其中证明 Transformer 是最精确的。我还发现，Facebook 移动性数据集对于预测确诊病例数最有用。然而，我发现，来自不同来源的数据集在预测新冠感染导致的死亡人数方面效果并不理想。
