Statistical Methods for Estimating Cure Fraction of COVID-19 Patients In India

Abstract

The human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people are recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors (cured population) in the context of lifetime data analysis. In this article, we present some statistical methods to estimate the cure fraction of the COVID-19 patients in India. Proportional hazards mixture cure model is used to estimate the cure fraction and the effect of covariates gender and age on lifetime. The data available on website https://api.cvoid19india.org is used in this study. We can see that, the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

Factors associated with survival of Iranian patients with COVID-19: comparison of Cox regression and mixture cure model

“…Few studies across the world have been performed to determine factors associated with the survival of COVID-19 patients using the mixture cure model. A study applied this model on Indian patients and showed that patients' age was associated with odds of cure, which was in accordance with the results of our study [25]. Undoubtedly, identification of factors related to the cure and severity of the disease can effectively contribute to effective patient management and provide care for patients who display these risk factors in order to increase the rate of cure and survival.…”

Cure models to estimate time until hospitalization due to COVID-19. A case study in Galicia (NW Spain)

“…Due to the intrinsic features of our data, showing some limitations such as the discretization of the times, the high rate of censored data and a foreseeable high cure probability, it is possible that semiparametric models are not a good choice for analyzing this data. Nonetheless, they have been previously used for other COVID-19 studies, specifically semiparametric Cox PH models, which also considered the influence of the age on the survival [59].…”

Study design synopsis: Survive the race – Time‐to‐event analysis

“…Survival models that relax this assumption are commonly referred to as cure models 20 . Cure models are currently popular for studying the time to death due to COVID‐19 because some individuals have acquired immunity and will not experience the disease in the future, even if they are observed for a long period of time 21 …”