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

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.