Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved]
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 agr...
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| Format: | Article |
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F1000 Research Ltd
2025-07-01
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| Online Access: | https://f1000research.com/articles/12-126/v3 |
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| author | Lina AlTamimi Liana Dalaeen Anas Khaleel Wael Abu Dayyih Abdallah Ahmed Elbakkoush Mohammad Niazi Baker Albadareen Zainab Zakaraya Alhareth Ahmad |
| author_facet | Lina AlTamimi Liana Dalaeen Anas Khaleel Wael Abu Dayyih Abdallah Ahmed Elbakkoush Mohammad Niazi Baker Albadareen Zainab Zakaraya Alhareth Ahmad |
| author_sort | Lina AlTamimi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bf70e111c4364682ae7ae0a1797ea9f2 |
| institution | Kabale University |
| issn | 2046-1402 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | F1000 Research Ltd |
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| series | F1000Research |
| spelling | doaj-art-bf70e111c4364682ae7ae0a1797ea9f22025-08-23T01:00:01ZengF1000 Research LtdF1000Research2046-14022025-07-011210.12688/f1000research.129799.3185108Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved]Lina AlTamimi0Liana Dalaeen1https://orcid.org/0000-0003-3446-2863Anas Khaleel2https://orcid.org/0000-0001-7584-2438Wael Abu Dayyih3https://orcid.org/0000-0002-5832-7247Abdallah Ahmed Elbakkoush4https://orcid.org/0000-0002-6551-1317Mohammad Niazi5Baker Albadareen6https://orcid.org/0000-0002-6281-5593Zainab Zakaraya7Alhareth Ahmad8Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, JordanFaculty of Pharmacy, Mutah University, Karak-61710, JordanDepartment of Pharmacology and Biomedical Sciences, Faculty of Pharmacy, Petra University, Amman, JordanFaculty of Pharmacy, Mutah University, Karak-61710, JordanLi Chen Biomedical Company, Taipei, TaiwanDepartment of Pharmacology and Biomedical Sciences, Faculty of Pharmacy, Petra University, Amman, JordanDepartment of Applied Mathematics, Faculty of Applied Science, Palestine Technical University – Kadoorie, Tulkarm, Palestinian TerritoryBiopharmaceutics and Clinical Pharmacy Department, Faculty of Pharmacy, AL-Ahliyya Amman University, Amman, JordanBiopharmaceutics and Clinical Pharmacy Department, Faculty of Pharmacy, AL-Ahliyya Amman University, Amman, JordanBackground 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.https://f1000research.com/articles/12-126/v3COVID-19 Google Sheet Logistic regression model Sex Age Smokingeng |
| spellingShingle | Lina AlTamimi Liana Dalaeen Anas Khaleel Wael Abu Dayyih Abdallah Ahmed Elbakkoush Mohammad Niazi Baker Albadareen Zainab Zakaraya Alhareth Ahmad Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] F1000Research COVID-19 Google Sheet Logistic regression model Sex Age Smoking eng |
| title | Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] |
| title_full | Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] |
| title_fullStr | Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] |
| title_full_unstemmed | Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] |
| title_short | Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan [version 3; peer review: 2 approved] |
| title_sort | predicting infection with covid 19 disease using logistic regression model in karak city jordan version 3 peer review 2 approved |
| topic | COVID-19 Google Sheet Logistic regression model Sex Age Smoking eng |
| url | https://f1000research.com/articles/12-126/v3 |
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