Higher Frequency of Healthcare Professionals is Associated With a Low Incidence of COVID-19-Related Death.

The COVID-19 pandemic has affected over 200 countries with varying levels of infection and mortality rates. To understand the impact of healthcare resources and cultural factors, a cross-sectional study was conducted on 76 countries. The study used K-means clustering to identify 2 distinct clusters and performed a Welch's test to compare different parameters. The countries were then plotted on the Inglehart-Welzel global cultural map. By incorporating this framework, researchers can systematically scrutinize the intricate interplay of cultural factors. This will provide valuable context for understanding individuals' behaviors, preferences, and decision-making as they pertain to the challenges posed by COVID-19 and its mitigation strategies. The results showed that countries with higher levels of healthcare professionals had a lower death rate, even with a relatively high rate of infection. These countries also had higher levels of individual self-expression. The study highlights the importance of adhering to recommended protocols, as neglect can result from a lack of self-expression, leading to an increase in the spread of communicable diseases. It also emphasizes crucial role of healthcare professionals in managing crisis related to the pandemic.

COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features.

Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.