Relationships between Risk Factors and the Death from COVID-19: We examined 106,103,566 COVID-19 cases from 20 countries, revealing that individuals aged over 65 years represented 76.51% of the deaths, frailty and pre-frailty (an intermediate stage between normal and frail) accounted for 83.20%, hypertension for 63.44%, cardiovascular diseases for 42.64%, dyslipidemia for 37.33%, diabetes for 35.44%, heart diseases for 28.63%, arrhythmias for 27.11%, depression for 26.84%, coronary artery disease for 25.48%, dementia for 23.18%, and renal disease for 18.86% of the fatalities (Figure 1A).
Among the elderly patients (a total of 38,645,762 cases), hyperlipidemia was present in 49.21% of the cases, hypertension in 48.22%, gastroesophageal disease in 46.98%, heart disease in 27.47%, cerebrovascular disease in 22.97%, diabetes in 20.27%, heart failure in 15.94%, depression in 14.85%, renal disease in 14.68%, and cancer in 12.91% (Figure 1B). In contrast, among patients with frailty (a total of 890,586 cases), hypertension was a factor in 63.89% of the cases, rheumatoid arthritis in 36.31%, high blood cholesterol in 36.11%, osteoporosis in 28.18%, heart disease in 26.87%, diabetes in 25.47%, depression in 23.57%, dementia in 20.13%, renal disease in 19.29%, and coronary artery disease in 18.57% (Figure 1C).
To further investigate the relationship between aging, frailty, and mortality in COVID-19 patients, we analyzed the number of underlying diseases these patients had. A nearly 75% similarity was observed in the proportion of patients with frailty who had more than two underlying diseases to that of COVID-19 related deaths with two underlying diseases (Figure 1D). In comparison, the older adult group had an approximate proportion of 62.59%. This finding suggests that the presence of two or more underlying diseases in patients with frailty can serve as a useful indicator for determining the risk of death in COVID-19 patients.
By examining clinical data and assessing the proportion of vaccination history among COVID-19-related deaths (7,348,213 total cases), we found that less than 20% of the deceased individuals were vaccinated (Figure 1E). This outcome indicates that vaccination history also serves as a valuable predictor for evaluating the risk of death in COVID-19 patients.
The Formula Proposed for Predicting the Deaths in COVID-19: Based on the analysis results and the logical relationships among population factors, we derived a mathematical formula to estimate the potential number of deaths as follows: A = A1 × A2 × (1 − A3) × A4. This equation implies that the high-risk population (i.e., the deaths) can be calculated by multiplying the total infections by the proportion of individuals over 60 years, then by (1 − vaccination rate), and finally by the proportion of frail elderly individuals (Figure 2A). The estimated number of high-risk populations closely aligns with the actual number of COVID-19 deaths in areas with strict control measures in place, such as New Zealand, and Singapore (Figure 2B), as well as in areas with less stringent control measures, including the USA, Brazil, India, Italy, England, Germany, and Spain, Republic of Korea, Russia, Australia, Iran, Mexico, Türkiye, Viet Nam, and the Netherlands (Figure 2C). To evaluate the accuracy of our prediction, we assessed the difference between our predicted values and the reported numbers of deaths. We found that the highest discrepancy was 42.06% and the lowest was 2.60%, resulting in a mean variation of 16.24% (Figure 2D).
Software Developed for Assessing the Risk of Death for a Population Suffering from COVID-19: We developed a software tool designed to assess the risk of mortality within a population, providing a reference framework for decision-making at country or regional levels. This software incorporates our risk assessment formula into the traditional SEIR model, which is frequently employed for simulating the progression of an epidemic (Figure 3A). Our software offers both out-of-the-box and customizable functionality (Figure 3B). Users can input relevant epidemic parameters and click the “simulate” button to obtain the predicted total infection and death counts.
Using data from the United States between December 1, 2021 and March 1, 2022, we entered these values and executed the software. The 90-day simulation results are presented within the software interface and can be exported for further analysis (Figure 3C). We compared the predicted and reported numbers of total infections and deaths. The reported figures were 30,140,720 and 168,655, whereas the simulated figures were 30,263,302 and 186,820, respectively (Figure 3D). These findings demonstrate the practical utility of our software tool.
Scale Designed for Assessing the Risk of Death for an Individual Suffering from COVID-19: We designed a scale that considers the above main key factors that lead to COVID-19 deaths to rapidly assess the risk of death for an individual. This scale consists of four domains: frailty phenotype, comorbidity number, vaccine dose, and COVID-19 symptom (Figure 4A). We can assess the risk by way of the question and answer scoring (Figure 4B). It has 6 questions and 14 answers, and the score is rated from 0 to 6 (Supplementary Table S2). The evaluation results are as follows: low risk at <3.5; moderate risk at 3.5–5; and high risk at >5. Presently, we are in the information age. We have designed an online web page for risk assessment anytime and anywhere to facilitate individual prediction (Figure 4C). Completing this scale may only take 1–3 minutes due to its free and out-of-the-box availability, which will allow a simple and quick individual assessment.