数据质量模型用于评估公共COVID-19大数据集
Alladoumbaye Ngueilbaye1,2, Joshua Zhexue Huang1,2, Mehak Khan3
1Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 Guangdong China.
概括
本研究介绍了一种数据质量模型,用于评估中非COVID-19报告. 该模型确定了数据质量问题,这些问题对于公共卫生决策和大数据分析至关重要.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 高质量的数据对于基于证据的医疗保健和公共卫生决策至关重要.
- 准确和可访问的COVID-19数据报告对从业者和研究人员来说至关重要.
- 现有的国家COVID-19数据报告系统在疫情期间显示出广泛的质量缺陷.
研究的目的:
- 为评估COVID-19数据报告提出和评估数据质量模型.
- 为了确定世界卫生组织 (WHO) 在中非经济和货币共同体 (CEMAC) 地区的COVID-19报告中的数据质量问题.
- 建议改善COVID-19数据质量的潜在解决方案.
主要方法:
- 开发一个数据质量模型,包括一个正规数据模型,四个充足度级别和本福德定律.
- 该模型的应用用于评估世卫组织在六个CEMAC国家报告的COVID-19数据.
- 数据分析涵盖了2020年3月6日至2022年6月22日期间的数据.
主要成果:
- 拟议的数据质量模型有效地确定了COVID-19数据输入中的质量问题.
- 该模型的充分性水平作为可靠性指标用于大数据集检查.
- 在评估的COVID-19报告中发现了重要的数据质量挑战.
结论:
- 开发的数据质量模型对于评估大数据分析输入数据是有效的.
- 解决已识别的数据质量问题对于可靠的公共卫生监测至关重要.
- 需要进一步的研究和跨学科的合作来完善和扩展该模型的应用.
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