Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model

Abstract

Objective To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19). Methods In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model. Results A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease. Conclusions The log-logistic AFT model accurately described the recovery time of patients with COVID-19.

On-Admission Anemia and Survival Rate in COVID-19 Patients

“…In these models, median survival times and also TR rather than HR are calculated [ 19 ] . The TR greater than one denotes that the effect of a covariate prolongs survival time and TR less than one denotes a shorter survival time [ 23 ] . In survival analysis, AFT models have widely been used to model diseases, including acute liver failure, leukemia, thalassemia, and cancers [ 24 , 25 ] .…”

“…In survival analysis, AFT models have widely been used to model diseases, including acute liver failure, leukemia, thalassemia, and cancers [ 24 , 25 ] . These models have also been applied to determine factors such as recovery time, hospital discharge, intubation periods, and the serial intervals of COVID-19 cases [ 23 , 26 , 27 ] . However, limited studies have been focused on investigating the relationship of at admission Hb levels at admission with COVID-19 mortality [ 3 , 28 ] , and this problem is still insufficiently explored.…”

Modeling infections and deaths averted due to COVID-19 vaccination strategies in Ghana

“…The mean pre-symptomatic period, 1/c, is assumed to be 2.9 (7). The mean duration of infectiousness for symptomatic individuals (1/f) is 15.7 days, and that of asymptomatic individuals (1/q) is 7.25 days (8)(9)(10)(11). The transmission rate, β, is estimated from the basic reproduction number (R0) using the formula (1 − 𝛿𝛿) � 𝑢𝑢𝛽𝛽 𝑞𝑞 � + 𝛿𝛿( 𝑟𝑟𝛽𝛽 𝑐𝑐 + 𝛽𝛽 𝑓𝑓 ) (12), assuming a value of 3.13 for the initial strain as assessed by Armachie and colleagues (13).…”

“…Hence, the government's goal to reach widespread vaccine coverage by October 2021 was not met (6). Studies on COVID-19 vaccine hesitancy among Ghanaians reported that more than 35% of participants said they would not receive the vaccine because of concerns about vaccine efficacy and conspiracy theories (7,10). Moreover, a seroprevalence study found that 19% of Ghanaians tested positive to anti-SARS-CoV-2 IgM or IgG or both in August 2020 (11).…”

Examining the Impact of Increasing Vaccine Coverage and Nonpharmaceutical Interventions against Coronavirus Disease 2019 In Ghana using Mathematical Modeling

“…31 The mean duration of infectiousness is 15.7 days (95% CI: 13, 18.4) and 7.25 days (95% CrI: 5.1, 8.69) for symptomatic individuals (1/f) and asymptomatic individuals (1/h), respectively. [32][33][34][35] The transmission coefficient, β, is estimated from the basic reproduction number (R0), which is assumed to be 3.13 (95% CrI: 1.24, 6.35) as reported by Armachie and colleagues for the initial strain in Ghana. 36 According to the Centers for Disease Control and Prevention (CDC) COVID-19 pandemic planning scenarios, the relative transmissibility of asymptomatic and presymptomatic individuals, u and r, are assumed to be 0.75, respectively.…”