Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan.

Q2 Pharmacology, Toxicology and Pharmaceutics

F1000Research Pub Date : 2025-07-30 eCollection Date: 2023-01-01 DOI:10.12688/f1000research.129799.3

Anas Khaleel, Wael Abu Dayyih, Lina AlTamimi, Liana Dalaeen, Zainab Zakaraya, Alhareth Ahmad, Baker Albadareen, Abdallah Ahmed Elbakkoush

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引用次数: 0

Abstract

Background: On March 2020, World Health Organization (WHO) labeled coronavirus disease 2019 (COVID-19) as a pandemic. COVID-19 has rapidly increased in Jordan which resulted in the announcement of the emergency state on March 19th, 2020. Despite the variety of research being reported, there is no agreement on the variables that predict COVID-19 infection. This study aimed to test the predictors that probably contributed to the infection with COVID-19 using a binary logistic regression model.

Methods: Based on data collected by Google sheet of COVID-19 infected and non-infected persons in Karak city, analysis was applied to predict COVID-19 infection probability using a binary logistic regression model.

Results: A total of 386 participants have completed the questionnaire including 323 women and 63 men. Among the participants 295 (76.4%) were aged less than or equal 45 years old, and 91 (23.6%) were aged over 45 years old. Among the 386 participants a total of 275 were infected with COVID-19. The Logistic regression test was used to analyze every demographic characteristic (sex, age, job, smoking, chronic disease, yearly flu injection) in this study to find predictors of the likelihood of COVID-19 infection. The findings indicate that the participants' sex and age are the most important demographic determinants of infection. Female gender was associated with higher infection risk compared to males (OR = 2.04, 95% CI: 1.17-3.58, p = 0.012). Participants aged >45 years had increased infection risk compared to those ≤45 years (OR = 1.91, 95% CI: 1.11-3.30, p = 0.020). Cox & Snell R Square (R2 = 0.028) and Nagelkerke R Square (R2 = 0.039) indicators were used to measure model fineness with a significant P-value < 0.05.

Conclusions: Given a person's age and sex, the final model presented in this study can be used to calculate the probability of infection with COVID-19 in Karak city. This could help aid health-care management and policymakers in properly planning and allocating health-care resources.

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利用logistic回归模型预测约旦卡拉克市COVID-19感染

背景：2020年3月，世界卫生组织（WHO）将2019冠状病毒病（COVID-19）列为大流行。2019冠状病毒病在约旦迅速蔓延，导致约旦于2020年3月19日宣布进入紧急状态。尽管报道了各种各样的研究，但对预测COVID-19感染的变量尚无一致意见。本研究旨在使用二元逻辑回归模型测试可能导致COVID-19感染的预测因素。方法：根据卡尔克市新冠肺炎感染者和非感染者谷歌表采集的数据，采用二元logistic回归模型对新冠肺炎感染概率进行分析预测。结果：共完成问卷386人，其中女性323人，男性63人。其中年龄小于等于45岁的295人（76.4%），年龄大于45岁的91人（23.6%）。在386名参与者中，共有275人感染了COVID-19。使用Logistic回归检验分析本研究中的每个人口统计学特征（性别、年龄、工作、吸烟、慢性疾病、每年注射流感疫苗），以寻找COVID-19感染可能性的预测因素。研究结果表明，参与者的性别和年龄是感染最重要的人口统计学决定因素。与男性相比，女性感染风险较高（OR = 2.04, 95% CI: 1.17-3.58, p = 0.012）。与≤45岁的参与者相比，年龄在bb0 - 45岁之间的参与者感染风险增加（OR = 1.91, 95% CI: 1.11-3.30, p = 0.020）。采用Cox & Snell R Square （R2 = 0.028）和Nagelkerke R Square （R2 = 0.039）指标衡量模型精细度，p值均< 0.05。结论：在给定人的年龄和性别的情况下，本研究提出的最终模型可用于计算卡拉克市感染COVID-19的概率。这有助于保健管理部门和决策者妥善规划和分配保健资源。

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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来源期刊

F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)

CiteScore

5.00

自引率

0.00%

发文量

1646

审稿时长

1 weeks

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