This study investigated how wealth, development, and policy conditions affected the performance of 168 countries regarding the number of confirmed COVID-19 cases and deaths per million inhabitants after 365 days from the first confirmed case. Supervised and unsupervised machine learning techniques were used, including clustering to explore the data, principal component analysis to deepen the understanding of the data, and principal component analysis and multilevel modeling to confirm the identified relationships and patterns. The results indicated that pre-existing wealth, development, and policy conditions exerted a significant impact on the performance patterns of the analyzed countries in relation to the pandemic. It is concluded that the COVID-19 pandemic revealed the complex interactions between economic, social, and public health policy variables, demonstrating that, even with access to a wide range of resources, rich and developed countries faced high numbers of cases and deaths, highlighting the need for more equitable public health policies prepared for future health emergencies.
