AbstractBackgroundThe rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely populated areas.ObjectiveAiming at bringing more light on key economic and population health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly available COVID-19 datasets.MethodsWe have applied Pearson Correlation Analysis and Clustering Analysis (X-Means Clustering) techniques on the data obtained by combining multiple datasets related to country economics, medical system & health, and COVID-19 - related statistics. The resulting dataset consisted of COVID-19 Case and Mortality Rates, Economic Statistics, and Population Public Health Statistics for 165 countries reported between 22 January 2020 and 28 March 2020. The correlation analysis was conducted with the significance level α of 0.05. The clustering analysis was guided by the value of Bayesian Information Criterion (BIC) with the bin value b = 1.0 and the cutoff factor c = 0.5, and have provided a stable split into four country-level clusters.ResultsThe study showed and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study identified potential COVID-19 under-reporting traits, as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we also described the four disease development scenarios, which are tightly knit to country-level economic and population health factors. Finally, we highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.ConclusionsIn this study, we first identified possible COVID-19 reporting issues and biases across different countries and regions. Second, we identified crucial factors affecting the speed of COVID-19 disease spread and provided recommendations on choosing and operating economic and health system factors when analyzing COVID-19 progression. Particularly, we discovered that the political system and compliance with international disease control norms are crucial for effective COVID-19 pandemic cessation. However, the role of some widely-adopted measures, such as GHS Health Index, might have been overestimated in lieu of multiple biases and underreporting challenges. Third, we benchmarked our findings against the widely-adopted Global Health Security (GHS) model and found that the latter might be redundant when measuring and forecasting COVID-19 spread, while its individual components could potentially serve as stronger COVID-19 indicators. Fourth, we discovered four clusters of countries characterized by different COVID-19 development scenarios, highlighting the differences of the disease reporting and progression in different economic and health system settings. Finally, we provided recommendations on sophisticated measures and research approaches to be implemented for effective outbreak measurements, evaluation and forecasting. We have supported the latter recommendations by a preliminary regression analysis based on the our-collected dataset. We believe that our work would encourage further in-depth quantitative research along the direction as well as would be of support to public policy development when addressing the COVID-19 crisis worldwide.
Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease
