Data quality model for assessing public COVID-19 big datasets

Abstract

For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems’ efficacy has not been thoroughly evaluated. However, the current COVID-19 pandemic has shown widespread flaws in data quality. We propose a data quality model (canonical data model, four adequacy levels, and Benford’s law) to assess the quality issue of COVID-19 data reporting carried out by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries between March 6,2020, and June 22, 2022, and suggest potential solutions. These levels of data quality sufficiency can be interpreted as dependability indicators and sufficiency of Big Dataset inspection. This model effectively identified the quality of the entry data for big dataset analytics. The future development of this model requires scholars and institutions from all sectors to deepen their understanding of its core concepts, improve integration with other data processing technologies, and broaden the scope of its applications.

An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis

“…Using the suggested model, missing values in the data stream are predicted. Also, in [20], a data quality model comprising a canonical data model, four adequacy levels, and Benford's law is proposed to assess the quality of COVID-19 data reported by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries. The model provides insights into the sufficiency and dependability of big dataset inspection.…”