Kakamen Aveen, Hamarash Ibrahim Ismael
Department of Computer Science and Engineering, School of Science and engineering, University of Kurdistan-Hewler, Iraq.
Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Kurdistan, Iraq.
Data Brief. 2025 Jun 17;61:111785. doi: 10.1016/j.dib.2025.111785. eCollection 2025 Aug.
Vaccination has long been recognized as the most effective means for disease prevention, yet concerns have raised globally regarding possible side effects. These concerns were very serious with the COVID-19 vaccines due to the accelerated development process, which did not follow the traditional vaccine lifecycle. This paper describes a dataset that captures a range of long-term symptoms in patients after recovering from the virus including information on vaccination status and self-reported memory issues. The research was carried out in the Kurdistan region of Iraq, and as a result, The Kurdistan Long Term COVID Dataset (KLTCD) was established. The dataset is publicly available online. Basic statistical analysis, measures and machine learning logistic algorithm have been used to evaluate the hypothesis. The established dataset contains numerous features, variables, and labels, making it one of the most information-rich datasets on long-term COVID-19. It may be used to support or refute various hypothesis related to the virus.
长期以来,疫苗接种一直被认为是预防疾病的最有效手段,但全球对可能的副作用也存在担忧。由于开发过程加速,未遵循传统疫苗的生命周期,这些担忧在新冠疫苗方面尤为严重。本文描述了一个数据集,该数据集记录了患者从病毒中康复后的一系列长期症状,包括疫苗接种状况信息和自我报告的记忆问题。这项研究在伊拉克库尔德地区进行,由此建立了库尔德长期新冠数据集(KLTCD)。该数据集可在网上公开获取。已使用基本统计分析、度量方法和机器学习逻辑算法来评估假设。已建立的数据集包含众多特征、变量和标签,使其成为关于长期新冠的信息最丰富的数据集之一。它可用于支持或反驳与该病毒相关的各种假设。