A Study on Survival Scenario of COVID-19 patients in India: An Application of Survival Analysis on patient demographics

Kundu, Sampurna; Kirti,; Mandal, Debarghya

doi:10.1101/2020.08.01.20162115

Cited by 2 publications

(2 citation statements)

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“…Also, Oulhaj et al (2020) investigated the bias (through extensive simulations) in estimating the hazard ratio (HR) and the absolute risk reduction (ARR) of death when competing risks are ignored. Furthermore, Thai et al (2020), Salinas-Escudero et al (2020 and Kundu and Mandal (2020) employed survival analysis techniques to evaluate COVID-19 risk assessments in Vietnamese, Mexican and Indian populations, respectively. In the realms of exploring machine learning techniques, Atlam et al (2021) proposed a combination of autoencoder deep neural network and Cox regression to enhance prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Implementing survival analysis Castelnuovo et al (2020) found that impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy. Kundu and Mandal (2020) carried out a survival analysis to establish the variability in survivorship among age groups and sex at different levels that is national, state and district level in india. Ali et al (2020) implemented survival analysis to determine risk factors for death in patients with COVID-19 admitted to the main public sector hospital in Somalia and identify interventions contributing to improved clinical outcome in a low-resource and fragile setting.…”

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confidence: 99%

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Factors Influencing The First and Second Peak of COVID-19 Global Cases: A Survival Analysis

Oshan

Zainab

Bandyopadhyay

et al. 2021

Preprint

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Objectives: The number of reported cases continues to increase everyday, since the first case of COVID-19 was detected in Wuhan, China in December 2019. Using the global COVID-19 data of 188 countries extracted from the Our World in Data between January 22, 2020--January 18, 2021, this study attempts to explore the potential determinants of the number of days to reach the first and second peak of COVID-19 cases for all 188 countries. Methods: A semi-parametric Cox proportional hazard (PH) model has been used to explore the covariates that are associated with the number of days to reach the first and second peak of global COVID-19 cases. Results: As of January 18, 2021, the first and second peak were found in 175 and 59 countries, out of 188 countries, respectively. The median number of days to hit the first peak was 60 days for countries which have median age above 40 while the median number of days to hit the second peak was 267 days for countries which have population density above 500 per square kilometer. Countries having population density between 250 and 500 were 2.25 times more likely to experience the first peak of COVID-19 cases (95% CI: 1.15-4.45, P<0.05) than countries which have population density below 25. Countries having population density between 100 and 250 were 67% less likely to get the second peak (95% CI: 0.119-0.908, P<0.05) compared to countries which have population density below 25. Countries having cardiovascular death rates above 350 were 2.94 times more likely to get the first peak (95% CI: 1.59-5.43, P<0.001). In contrast, countries having diabetes prevalence rate 3 to 12 were 85% less likely to experience the second peak of COVID-19 cases (95% CI: 0.036-0.680, P<0.05) than countries which have diabetes prevalence rate below 3. Besides, highly significant difference is found in the Kaplan-Meier plots of the number of days to reach both peaks across different categories of the country's Human Development Index. Conclusions: The number of days to the first peak was considerably small in Asian & European countries but that to the second peak in the countries where diabetes prevalence was very higher. Country's life expectancy had a significant effect on determining the first peak and so was the case for two other variables--the cardiovascular death rate and hospital beds per thousand. A contrast result was found for Human Development Index factor under the second peak. Additionally, it was found that the second peak was more likely to occur in more densely populated countries.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%