Khaleel Anas, Abu Dayyih Wael, AlTamimi Lina, Dalaeen Liana, Zakaraya Zainab, Ahmad Alhareth, Albadareen Baker, Elbakkoush Abdallah Ahmed
Department of Pharmacology and Biomedical Sciences, Faculty of Pharmacy, Petra University, Amman, Jordan.
Faculty of Pharmacy, Mutah University, Karak-61710, Jordan.
F1000Res. 2023 Apr 3;12:126. doi: 10.12688/f1000research.129799.2. eCollection 2023.
: 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. We have analyzed the data collected from Karak city citizens to predict the probability of infection with COVID-19 using binary logistic regression model. 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. The ultimate logistic regression model provides the formula of COVID-19 infection probability based on sex and age variables. 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.
2020年3月,世界卫生组织(WHO)将2019冠状病毒病(COVID-19)列为大流行病。COVID-19在约旦迅速蔓延,导致2020年3月19日宣布进入紧急状态。尽管有各种各样的研究报告,但对于预测COVID-19感染的变量尚无定论。我们分析了从卡拉克市市民收集的数据,使用二元逻辑回归模型预测感染COVID-19的概率。基于通过谷歌表格收集的卡拉克市COVID-19感染者和未感染者的数据,应用分析方法使用二元逻辑回归模型预测COVID-19感染概率。最终的逻辑回归模型提供了基于性别和年龄变量的COVID-19感染概率公式。根据一个人的年龄和性别,本研究中提出的最终模型可用于计算卡拉克市感染COVID-19的概率。这有助于协助医疗保健管理部门和政策制定者合理规划和分配医疗保健资源。