COVID-19 (novel coronavirus disease) currently has no vaccine, and countries still need to implement preventive policies. Scholars from National Yang-Ming University and Sun Yat-sen University have collected big data from 169 countries worldwide to study the correlation between COVID-19 mortality rate and various factors, providing a basis for future decisions on implementing universal screening policies.
The research team, led by Professor Wu Junying from the Institute of Biomedical Informatics at Yang-Ming University and Assistant Professor Liang Liling from the Department of Business Administration at Sun Yat-sen University, analyzed cross-national big data from 169 countries with a total population of 7.5 billion. They investigated factors related to the COVID-19 mortality rate, including testing rates, government efficiency, population demographics, hospital bed capacity, and transportation accessibility.
According to Professor Wu Junying, reducing the COVID-19 mortality rate remains the highest priority. Therefore, big data research primarily focused on this aspect. The study found that countries with higher testing rates, higher government efficiency indices, and more hospital beds tend to have lower mortality rates. On the other hand, countries with an aging population and higher transportation accessibility tend to have higher mortality rates. These findings offer valuable insights for policymakers to consider when formulating strategies to combat the COVID-19 pandemic.
However, Professor Wu Junying further explains that the utility of testing varies in different countries depending on their unique circumstances. For countries with lower government efficiency, younger populations, and limited hospital bed capacity, increasing the number of tests may be more effective in reducing the mortality rate.
In the case of Taiwan, Wu Junying points out that the country has good government efficiency, an adequate number of hospital beds, and an aging population. Therefore, simply increasing the number of tests may not necessarily lower the mortality rate. To determine the optimal testing strategy, big data can be used to calculate the appropriate ratio of testing to the population, finding the “sweet spot” for universal screening.
Liang Liling also notes that the global average COVID-19 mortality rate is around 3.7%. However, in high-income countries, it can be as high as 4.9% due to their aging populations. In contrast, Taiwan’s mortality rate is only 1.5%, which is lower than the predicted model. This suggests that Taiwan’s effective government measures, self-management by its citizens, and high-quality healthcare system may contribute significantly to lowering the mortality rate.
Wu Junying adds that other factors, such as different comorbidities like diabetes and hypertension, may also play a role in the mortality rate of COVID-19. Analyzing the impact of these factors will be a focus for future research.
Said research findings have been published in the international journal Scientific Reports, providing valuable reference data for governments worldwide to decide whether to implement universal screening in their response to the pandemic.
