A new flexible regression model with application to recovery probability Covid-19 patients

Abstract

The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and on the recovery probabilities of Chinese individuals with COVID-19. We add new properties of the generalized Maxwell model. The coefficients of the regression and the recovered fraction are estimated by maximum likelihood and Bayesian methods. Further, some simulation studies are done to compare the regressions for different scenarios. Model-checking techniques based on the quantile residuals are addressed. The estimated survival functions for the patients are reported by age range and sex. The simulation study showed that mean squared errors decay toward zero and the average estimates converge to the true parameters when sample size increases. According to the fitted model, there is a significant difference only in the age group on the lifetime of individuals with COVID-19. Women have higher probability of recovering than men and individuals aged $\ge$60 years have lower recovered probabilities than those who aged $<60$ years. The findings suggest that the proposed model could be a good alternative to analyze censored lifetime of individuals with COVID-19.

Keywords:

• Censored data
• COVID-19
• Maxwell distribution
• mixture model
• quantile residuals

Acknowledgements

We are very grateful to a referees and associate editor for helpful comments that considerably improved the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.worldometers.info/coronavirus/countries

2 See: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf

Additional information

Funding

We gratefully acknowledge financial support from CAPES and CNPq.