Statistical analysis and estimation of the cumulative distribution function of COVID-19 cure duration in Iraq

Behzad Mansouri, Sami Atiyah Sayyid Al-Farttosi, H. Mombeni, R. Chinipardaz
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Abstract

Abstract COVID-19 disease has aggressively affected all aspects of human life since late 2019. Hospital staff have been under unprecedented pressure from a large number of patients, and in some countries, the lack of space for patients at the height of the epidemic has reached a point where hospitals do not have the capacity to accept new patients. Therefore, studying the duration of treatment of COVID-19 patients is very important in managing the ability of treatment staff and hospital facilities. In this paper, the length of hospitalization of all COVID-19 patients in Al-Sadr General Hospital in Al-Amarah, Iraq, is statistically studied from March 2020 to April 2021. The cumulative distribution function (cdf) of the patients’ treatment duration is estimated using Birnbaum-Saunders (B-S) kernel estimator. This estimate allows us to estimate the probability of a patient’s stay in the hospital for a specified period of time. In this paper, we obtain an asymptotic confidence interval for the B-S kernel estimator. However, due to the dependence of the obtained confidence interval on the unknown cdf and its derivatives, we propose a bootstrap algorithm to calculate the confidence interval and use it for the length of hospital stay of COVID-19 patients.
伊拉克新冠肺炎治愈时间累积分布函数的统计分析与估计
自2019年底以来,COVID-19疾病已经严重影响了人类生活的方方面面。大量病人给医院工作人员带来了前所未有的压力,在一些国家,在疫情最严重的时候,由于病人空间不足,医院已经没有能力接收新的病人。因此,研究COVID-19患者的治疗时间对于管理治疗人员和医院设施的能力非常重要。本文对2020年3月至2021年4月伊拉克Al-Amarah Al-Sadr总医院所有COVID-19患者的住院时间进行统计研究。使用Birnbaum-Saunders (B-S)核估计器估计患者治疗持续时间的累积分布函数(cdf)。这个估计使我们能够估计病人在指定时间内住院的概率。本文给出了B-S核估计量的渐近置信区间。然而,由于得到的置信区间依赖于未知的cdf及其导数,我们提出了一种bootstrap算法来计算置信区间,并将其用于COVID-19患者的住院时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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