Survival Analysis of Covid-19 Patients Based on Time of Recovery

Abstract

Corona virus is a virus that can cause the respiratory tract to become infected, and this viral infection is called COVID-19. This virus spreads so fast that it has spread to several countries, including Indonesia. In Indonesia, COVID-19 was detected in early March, precisely on March 2, 2020. The uncertain increase in the number of COVID-19 patients will have an impact on society and the country. This condition is compounded by the high number of deaths due to the COVID-19 virus. Therefore, this study was conducted to analyze survival based on the healing rate of COVID-19 patients, in order to obtain information about the time period and the factors that cause a person with COVID-19 to survive. The method used in the survival analysis is the Kaplan-Meier test as a counter to the estimated recovery time of COVID-19 patients and the Log-Rank test to test for differences in the survival function of the recovery time of COVID-19 patients in the two groups. Kaplan-Meier and Log-Rank tests are part of the non-parametric method which is a statistical test that does not require any assumptions about the distribution of population data. The data used is data on COVID-19 patients at the Malahayati Hospital from January to May 31, 2021. The conclusion obtained is the survival function curve / length of time on the recovery rate of COVID-19 patients based on gender, age, and positive and suspected COVID-19 patients. with and without comorbidities. However, based on the Log-Rank test with = 0.05, it was concluded that there was no significant difference in the length of time for recovery of COVID-19 patients based on gender, age and positive patients and patients with suspected COVID-19 with comorbid and without comorbidities.

Time to Recovery From Covid-19 Among Patients in Sidoarjo Regency, East Java Province, Indonesia in 2022

“…Research focused on survival is crucial for understanding COVID recovery time and identifying factors that influence it. This knowledge can enhance the effectiveness of COVID-19 pandemic management and mitigation efforts by aligning programs with specific needs (5), including predicting the risk of death (9)(10)(11)(12). The results of survival analysis can assist in developing better approaches for diagnostic enforcement, management, and COVID-19 vaccine development, thereby improving efforts to combat the pandemic.…”