Impacts of data quality issues and biases on analysis of COVID-19 data in South Africa

Leevan Naidoo

Master's thesis

Abstract:

The COVID-19 pandemic demonstrated the significance of data-driven decision-making in responding to the virus. The impact of data quality and biases on COVID-19 analysis in South Africa is examined in this thesis. Research, expert interviews, and a survey highlighted various challenges in data reporting. These include limitations in availability, honesty, and data management practices. Data quality was hampered by a lack of integration between public and commercial systems, while misinformation and limited testing capacity impacted accuracy. In addition, the analysis contrasted expected and actual data, indicating underreporting of positive cases. Despite these obstacles, most South Africans believed that they took the necessary precautions to protect themselves from the virus, suggesting that the correct information was disseminated to them. Recommendations were also provided by interviewed experts to better manage the pandemic in the future. These include improving data collection and reporting systems, investing in health informatics training, implementing data quality assurance processes, and overall improved leadership from the South African government.

Abstract:

The COVID-19 pandemic demonstrated the significance of data-driven decision-making in responding to the virus. The impact of data quality and biases on COVID-19 analysis in South Africa is examined in this thesis. Research, expert interviews, and a survey highlighted various challenges in data reporting. These include limitations in availability, honesty, and data management practices. Data quality was hampered by a lack of integration between public and commercial systems, while misinformation and limited testing capacity impacted accuracy. In addition, the analysis contrasted expected and actual data, indicating underreporting of positive cases. Despite these obstacles, most South Africans believed that they took the necessary precautions to protect themselves from the virus, suggesting that the correct information was disseminated to them. Recommendations were also provided by interviewed experts to better manage the pandemic in the future. These include improving data collection and reporting systems, investing in health informatics training, implementing data quality assurance processes, and overall improved leadership from the South African government.

Keywords

COVID-19, Data Quality, Data Biases, South Africa, Pandemic Management, Decision-making, Data Reporting, Citizen Behavior, Data Analysis

Keywords

Data Quality, Data Biases, South Africa, Pandemic Management, COVID-19, Decision-making, Data Reporting, Citizen Behavior, Data Analysis

Language used: English

Date on which the thesis was submitted / produced: 28. 6. 2023

Thesis defence

• Date of defence: 28. 8. 2023
• Supervisor: Jan Kučera
• Reader: Martin Potančok

Vysoká škola ekonomická v Praze

Master programme:
Information Systems Management