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.

Based on strategic human capital theory, this study examines the effects of star surgeons on two different types of healthcare outcomes (i.e., number of surgical patients and length of patients’ in-hospital stay after surgery) in the surgery department. We also explore whether the relationship between star surgeons and healthcare outcomes is contingent on the expertise disparity between star and non-star surgeons. The results of an empirical analysis on colorectal cancer surgeons in 80 departments in South Korean hospitals show that the number of star surgeons increases the number of surgical patients and reduces the length of patients' stay after surgery. Moreover, the positive relationship between star surgeons and the number of surgical patients is strengthened when the expertise disparity between star and non-star surgeons is low. The implications of these findings for research and practice are also discussed.