COVID-19 Pandemic: a methodological model for the analysis of government’s preventing measures and health data records

The present study focuses on the period of the COVID-19 pandemic in Greece, Romania and Servia, from the detection of the first case of COVID-19 to the first week of 2022. In this study, a combination of multivariate data analysis methods was employed to analyze COVID-19-related data so as assess the quality of decision-making outputs during the crisis and improve evidence-based decision-making processes. For the study purposes, data were obtained from the Oxford Covid-19 Government Response Tracker (OxCGRT) and were combined with self-collected Covid-19 data and official data from ECDC. The Oxford Covid-19 Government Response Tracker (OxCGRT) collects publicly available information reflecting government response from 180 countries since 1 January 2020. The tracker is based on data for 23 indicators. In this study, two groups of indicators were considered: Containment & Closure and Health Systems. The first group of indicators refers to COVID-19: analysis of government’s preventing measures and health data records “collective” level policies and measures, such as school closures and restriction in mobility, while the second refers to “individual” level policies and measures, such as testing and vaccination. All collective-level indicators (C1 to C8) were summed to yield a total score (ranging from 0 to 16). Similarly, individual-level indicators (H1 to H3 and H6 to H8) were summed to compute a total score (ranging from to 12). The data about COVID indices refer to positive cases, number of Covid-19-related deaths, number of tests and total number of vaccinations administered. These data have been recorded daily since March 2020 from public announcements by official and verified sources. Three quantitative indicators were derived, a positivity index (#cases / #tests), a mortality index (#deaths / #cases) and a testing index (#tests / #people). The final data set consisted of five indicators: two ordinal total scores, and three quantitative indices. Based on the study results, we can argue that, when it comes to measures and real time data following a situation such as the pandemic, “the chicken and egg” dilemma arises. The question is whether “collective” and “individual” measures affect daily incidence data or the inverse (i.e., that the daily data lead to measures). We conclude that in fact the two should be perceived as working in conjunction and not independently from one another. The analysis showed that lower positivity rate is accompanied by average levels of measures from the government at both the “individual” and the “collective” level. Furthermore, higher positivity rate is accompanied by higher levels of measures, as a response. With regard to mortality rate, we observed that higher mortality invokes higher levels of “collective” measures and average levels of “individual” measures, whereas average levels of “collective” measures are associated with higher mortality rate. It is therefore evident that when it comes to decision making in crisis situations, a systematic collection, analysis and use of data is linked to more effective government response overall. Therefore, evidence-based policy making should be linked to crisis management.