A study on the quality of novel coronavirus (COVID-19) official datasets

Abstract

Policy makers depend on complex epidemiological models that are compelled to be robust, realistic, defendable and consistent with all relevant available data disclosed by official authorities which is deemed to have the highest quality standards. This paper analyses and compares the quality of official datasets available for COVID-19. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organisations based on the value of systematic measurement errors. We combined excel files, text mining techniques and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors, and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data.

COVID-19 surveillance - a descriptive study on data quality issues

“…Another limitation is the fact that we only studied the quality issues of COVID-19 data from one country, Portugal. However our results seem to be in line with the findings of Ashofteh and colleagues [7] who analysed and compared the quality of official datasets available for COVID-19, including data from the Chinese Center for Disease Control and Prevention, the World Health Organization,and the European Centre for Disease Prevention and Control. In fact, they also found noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories.…”

“…The potential consequences are suboptimal decision making or even not using data at all to drive decisions. Methodological challenges associated with analysing COVID-19 data during the pandemic, including access to high-quality health data, have been recognized [8] and some data quality concerns were described [7].…”

“…Only timely, high-quality data can provide valid and useful evidence for decision making and pandemic management. On the contrary, using datasets without carefully examining the metadata and documentation that describes the overall context of data can be harmful [7].…”

“…The potential consequences are suboptimal decision making or even not using data at all to drive decisions. Methodological challenges associated with analysing COVID-19 data during the pandemic, including access to high-quality health data, have been recognized [8] and some data quality concerns were described [7]. Nevertheless, to our knowledge, there is no study performing a structured assessment of data quality issues from the datasets provided by National Surveillance Systems for research purposes during the COVID-19 pandemic.…”

Generalised SEIR modelling of the COVID-19 pandemic course: data quality issues and structural analysis

“…Other researchers might use the methodology developed here to either calibrate non-error free or missing epidemic data. Indeed, as [39] points out, frequently the official databases are not exempt from measurement errors and/or missing data that must be accounted for and fixed before any further analysis. Estimating missing flows or the calibrated version of observed series can be done using the rationale behind the two-step calibration procedure proposed in this paper.…”