The Future Evolution of the Mortality Acceleration Due to the COVID-19: The Charlson Comorbidity Index in Stochastic Setting

Abstract

The empirical evidence from different countries point out many of those who die from coronavirus would have died anyway in the relatively near future due to their existing frailties or co-morbidities. The acceleration of the mortality conceives the underlying insight according to deaths are “accelerated” ahead of schedule due to COVID-19. Starting from this idea, we forecast the future mortality acceleration, based on the deterioration due to the presence of the comorbidities at COVID-19 diagnosis. Accordingly, we explicitly determine the contribution of each comorbidity on the acceleration forecasting, showing the future trend of the excess of deaths due to the COVID-19. To this aim, our proposal consists in developing a revised Charlson Comorbidity Index in a stochastic environment. Based on a post-stratification scheme, we obtain an unbiased comorbidity index that varies by age, centered on the reference population.

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

“…Similar to these studies, in our research, the number of comorbidities was associated with worse outcomes for patients and extended hospital stays. Chronic kidney disease, the incidence of acute kidney injury, and atrial fibrillation have been shown to be comorbidities associated with reduced survival in patients hospitalized for COVID-19 [73]. Other studies confirm that lymphopenia, often observed in cancer patients, is associated with a higher risk of mortality [74].…”

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

“…Similar to these studies, in our research, the number of comorbidities were associated with worse outcome for the patients and extended hospital stay. Chronic kidney disease, the incidence of acute kidney injury, and atrial fibrillation have been shown to be comorbidities associated with reduced survival in patients hospitalized for COVID-19 [77]. Other studies confirm that lymphopenia, often observed in cancer patients, is associated with a higher risk of mortality [78].…”

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

“…Different methods in time series analyses can be used, such as the hybrid machine learning approach (using multiple simple algorithms to complement and facilitate each other) to anticipate the number of infected people and mortality rate[ 20 ]; LR (based on using regression models enabling subject-matter interpretation of the data); Least Absolute Shrinkage and Selection Operator (a model that uses over regression methods for more accurate predictions); support vector machine (using optimal hyperplane in an N-dimensional space, separating the data points in different classes); exponential smoothing (forecasting univariate time series data) to determine the affected by the virus people and the deceased cases[ 21 ]; numerical modeling to assess the effect of the population age on the mortality rate[ 22 ]; numerical modeling methods such as polynomial regression (fitting of a nonlinear relationship between the value of something and the condition mean of other); Bayesian Edge (estimating probability influenced by the belief of the likelihood of a certain outcome) and long short-term memory (having the ability to learn long term sequences of observations) to estimate the prevalence of SARS-CoV-2 infection and to predict the scale of the pandemic along with the mortality rate[ 23 ]; a deep learning system for the prediction of the COVID-19 time series[ 24 ]; mathematical model about the spread of COVID-19[ 25 ]; a stochastic model considering comorbidities and age[ 26 ]; an SIQR model made stochastic, considering the uncertainty of infection progress[ 27 ]; a fractional-order dynamical system[ 28 ]; fractional calculus and natural decomposition[ 29 ]; Caputo-Fabrizio fractional derivative[ 30 , 31 ]; and a nonpharmaceutical intervention approach to reduce the outbreak of COVID-19[ 32 ] .…”