Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 2; peer review: 1 approved, 1 approved with reservations]

• Published in: F1000 research 2023, Vol.12, p.126
• Main Authors: Khaleel, Anas, Abu Dayyih, Wael, AlTamimi, Lina, Dalaeen, Liana, Zakaraya, Zainab, Ahmad, Alhareth, Albadareen, Baker, Elbakkoush, Abdallah Ahmed
• Format: Article
• Language: English
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.129799.2

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. We have analyzed the data collected from Karak city citizens to predict the probability of infection with COVID-19 using 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: The ultimate logistic regression model provides the formula of COVID-19 infection probability based on sex and age variables. 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.