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Data quality model for assessing public COVID-19 big datasets.

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.

The Journal of Supercomputing
|June 26, 2023
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Summary
This summary is machine-generated.

This study introduces a data quality model to evaluate COVID-19 reporting in Central Africa. The model identifies data quality issues, crucial for public health decision-making and big data analytics.

Keywords:
4ABenford’s lawCEMAC regionCOVID-19 big datasetCanonical data modelData quality model

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Area of Science:

  • Public Health
  • Data Science
  • Health Informatics

Background:

  • High-quality data is essential for evidence-based healthcare and public health decision-making.
  • Accurate and accessible COVID-19 data reporting is critical for practitioners and researchers.
  • Existing national COVID-19 data reporting systems have demonstrated widespread quality flaws during the pandemic.

Purpose of the Study:

  • To propose and evaluate a data quality model for assessing COVID-19 data reporting.
  • To identify data quality issues in COVID-19 reporting by the World Health Organization (WHO) in the Central African Economic and Monetary Community (CEMAC) region.
  • To suggest potential solutions for improving COVID-19 data quality.

Main Methods:

  • Development of a data quality model incorporating a canonical data model, four adequacy levels, and Benford's Law.
  • Application of the model to assess COVID-19 data reported by the WHO in six CEMAC countries.
  • Data analysis covering the period from March 6, 2020, to June 22, 2022.

Main Results:

  • The proposed data quality model effectively identified quality issues in COVID-19 data entry.
  • The model's adequacy levels serve as dependability indicators for big dataset inspection.
  • Significant data quality challenges were identified in the assessed COVID-19 reporting.

Conclusions:

  • The developed data quality model is effective for assessing big data analytics entry data.
  • Addressing identified data quality issues is crucial for reliable public health surveillance.
  • Further research and interdisciplinary collaboration are needed to refine and expand the model's application.