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Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model

Year 2024, Volume: 50 Issue: 1, 1 - 5, 24.04.2024

Caner Tanış

https://doi.org/10.35238/sufefd.1335965

Abstract

The new coronavirus COVID-19 is an infectious disease that started spreading globally in December 2019. Some symptoms are known to give clues as to whether the COVID-19 virus is infected. Therefore, the main purpose of this paper was to determine specific symptoms related to COVID-19 for the rapid diagnosis of COVID-19 cases. The data set consists of 25985 individuals including PCR results, 2 demographic properties (age, gender), and 5 symptoms such as headache, shortness of breath, sore throat, fever, and cough is considered in this study. We analyzed the relationship between these covariates and PCR results by binary logistic regression model. A total of 16405 (63.1%) individuals having to positive PCR results were included in this study. The research population was divided into two age groups (<60 and ≥60). The findings regarding the symptoms observed in COVID-19 patients can be listed as follows: Headache (25.8%), shortness of breath (2.2%), sore throat (11.2%), fever (16.3%), and cough (26.2%). The findings of binary logistic regression analysis show that any individual in the elder group has more probability of a positive PCR result approximately 1.6 times (odds ratio [OR]: 1.681), 95% confidence interval [CI]: 1.535-1.840). Also, an individual with symptoms of headache is approximately %7 more likely to have a positive PCR result than a nonexistent one (OR: 1.068, CI: 1.006-1.135).

Keywords

Binary logistic regression COVID-19 Infectious disease Symptoms Odds ratio

Thanks

The author is thankful to the Israel Ministry of Health for providing public access to anonymized Covid-19 patient records and to Prof. Dr. David Gurwitz for his help in obtaining data of Covid-19 patients.

References

  • Alimohamadi, Y., Sepandi, M., Taghdir, M. and Hosamirudsari, H., 2020, Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis, Journal of preventive medicine and hygiene, 61 (3), E304.
  • Choi, S. and Ki, M., 2020, Analyzing the effects of social distancing on the COVID-19 pandemic in Korea using mathematical modeling, Epidemiology and health, 42.
  • Feng, Z., Yu, Q., Yao, S., Luo, L., Zhou, W., Mao, X., Li, J., Duan, J., Yan, Z. and Yang, M., 2020, Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics, Nature communications, 11 (1), 4968.
  • Fleitas, P. E., Paz, J. A., Simoy, M. I., Vargas, C., Cimino, R. O., Krolewiecki, A. J. and Aparicio, J. P., 2020, Understanding the value of clinical symptoms of COVID-19. A logistic regression model, MedRxiv, 2020.2010. 2007.20207019.
  • Guan, W.-j., Ni, Z.-y., Hu, Y., Liang, W.-h., Ou, C.-q., He, J.-x., Liu, L., Shan, H., Lei, C.-l. and Hui, D. S., 2020, Clinical characteristics of 2019 novel coronavirus infection in China, MedRxiv.
  • Health, I. M. o., 2021, https://data.gov.il/dataset/covid-19/, [March 7].
  • Hills, S. and Eraso, Y., 2021, Factors associated with non-adherence to social distancing rules during the COVID-19 pandemic: a logistic regression analysis, BMC Public Health, 21 (1), 1-25.
  • Ki, M., 2020, Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea, Epidemiology and health, 42.
  • Liu, X.-q., Xue, S., Xu, J.-b., Ge, H., Mao, Q., Xu, X.-h. and Jiang, H.-d., 2022, Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China, Acta Pharmacologica Sinica, 43 (1), 64-75.
  • Sonoda, S., Kuramochi, J., Matsuyama, Y., Miyazaki, Y. and Fujiwara, T., 2021, Validity of clinical symptoms score to discriminate patients with COVID-19 from common cold out-patients in general practitioner clinics in Japan, Journal of clinical medicine, 10 (4), 854.
  • Tang, Z., Li, M., Chen, W., Ran, X., Li, H. and Chen, Z., 2021, Clinical symptoms of COVID-19 pneumonia in children: A protocol for systematic review and meta-analysis, Medicine, 100 (1).
  • WHO, 2020, Director-General’s Opening Remarks at the Media Briefing on Covid 19 - 11 March 2020.
  • WHO, 2023, https://covid19.who.int/, [February 22].
  • Xiong, D., Zhang, L., Watson, G. L., Sundin, P., Bufford, T., Zoller, J. A., Shamshoian, J., Suchard, M. A. and Ramirez, C. M., 2020, Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California, Epidemics, 33, 100418.
  • Xu, Y., Xu, Z., Liu, X., Cai, L., Zheng, H., Huang, Y., Zhou, L., Huang, L., Lin, Y. and Deng, L., 2020, Clinical findings in critically ill patients infected with SARS-CoV-2 in Guangdong Province, China: A multi-center, retrospective, observational study, MedRxiv, 2020.2003. 2003.20030668.
  • Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y. and Zhou, Y., 2020, Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis, International journal of infectious diseases, 94, 91-95.
  • Yupari-Azabache, I., Bardales-Aguirre, L., Rodriguez-Azabache, J., Barros-Sevillano, J. S. and Rodríguez-Diaz, A., 2021, COVID-19 mortality risk factors in hospitalized patients: A logistic regression model, Revista de la Facultad de Medicina Humana, 21 (1), 19-27.
  • Zhou, F., Chen, T. and Lei, B., 2020, Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression, arXiv preprint arXiv:2006.16942.

COVID-19 Tanısında Semptomların ve Demografik Özelliklerin Lojistik Regresyon Modeli ile Analizi

Year 2024, Volume: 50 Issue: 1, 1 - 5, 24.04.2024

Caner Tanış

https://doi.org/10.35238/sufefd.1335965

Abstract

Yeni koronavirüs COVID-19, Aralık 2019'da küresel olarak yayılmaya başlayan bulaşıcı bir hastalıktır. Bazı semptomların COVID-19 virüsünün enfekte olup olmadığına dair ipuçları verdiği bilinmektedir. Bu nedenle, bu makalenin temel amacı COVID-19 vakalarının hızlı teşhisi için COVID-19 ile ilgili spesifik semptomları belirlemektir. PCR sonuçları, 2 demografik özellik (yaş, cinsiyet) ve baş ağrısı, nefes darlığı, boğaz ağrısı, ateş ve öksürük gibi 5 semptomu içeren 25985 kişiden oluşan veri seti bu çalışmada dikkate alınmıştır. Bu ortak değişkenler ile PCR sonuçları arasındaki ilişki ikili lojistik regresyon modeli ile analiz edilmiştir. PCR sonucu pozitif olan toplam 16405 (%63,1) birey bu çalışmaya dahil edilmiştir. Araştırma popülasyonu iki yaş grubuna ayrılmıştır (<60 ve ≥60). COVID-19 hastalarında gözlenen semptomlara ilişkin bulgular şu şekilde sıralanabilir: Baş ağrısı (%25,8), nefes darlığı (%2,2), boğaz ağrısı (%11,2), ateş (%16,3) ve öksürük (%26,2). İkili lojistik regresyon analizi bulgularına göre, yaşlı gruptaki herhangi bir bireyin pozitif PCR sonucu alma olasılığı yaklaşık 1,6 kat daha fazladır (odds oranı [OR]: 1,681), %95 güven aralığı [CI]: 1.535-1.840). Ayrıca, baş ağrısı semptomları olan bir bireyin pozitif PCR sonucuna sahip olma olasılığı olmayanlara göre yaklaşık %7 daha fazladır (OR: 1.068, CI: 1.006-1.135).

Keywords

İkili lojistik regresyon COVID-19 Bulaşıcı hastalık Semptomlar Odds oranı

References

  • Alimohamadi, Y., Sepandi, M., Taghdir, M. and Hosamirudsari, H., 2020, Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis, Journal of preventive medicine and hygiene, 61 (3), E304.
  • Choi, S. and Ki, M., 2020, Analyzing the effects of social distancing on the COVID-19 pandemic in Korea using mathematical modeling, Epidemiology and health, 42.
  • Feng, Z., Yu, Q., Yao, S., Luo, L., Zhou, W., Mao, X., Li, J., Duan, J., Yan, Z. and Yang, M., 2020, Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics, Nature communications, 11 (1), 4968.
  • Fleitas, P. E., Paz, J. A., Simoy, M. I., Vargas, C., Cimino, R. O., Krolewiecki, A. J. and Aparicio, J. P., 2020, Understanding the value of clinical symptoms of COVID-19. A logistic regression model, MedRxiv, 2020.2010. 2007.20207019.
  • Guan, W.-j., Ni, Z.-y., Hu, Y., Liang, W.-h., Ou, C.-q., He, J.-x., Liu, L., Shan, H., Lei, C.-l. and Hui, D. S., 2020, Clinical characteristics of 2019 novel coronavirus infection in China, MedRxiv.
  • Health, I. M. o., 2021, https://data.gov.il/dataset/covid-19/, [March 7].
  • Hills, S. and Eraso, Y., 2021, Factors associated with non-adherence to social distancing rules during the COVID-19 pandemic: a logistic regression analysis, BMC Public Health, 21 (1), 1-25.
  • Ki, M., 2020, Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea, Epidemiology and health, 42.
  • Liu, X.-q., Xue, S., Xu, J.-b., Ge, H., Mao, Q., Xu, X.-h. and Jiang, H.-d., 2022, Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China, Acta Pharmacologica Sinica, 43 (1), 64-75.
  • Sonoda, S., Kuramochi, J., Matsuyama, Y., Miyazaki, Y. and Fujiwara, T., 2021, Validity of clinical symptoms score to discriminate patients with COVID-19 from common cold out-patients in general practitioner clinics in Japan, Journal of clinical medicine, 10 (4), 854.
  • Tang, Z., Li, M., Chen, W., Ran, X., Li, H. and Chen, Z., 2021, Clinical symptoms of COVID-19 pneumonia in children: A protocol for systematic review and meta-analysis, Medicine, 100 (1).
  • WHO, 2020, Director-General’s Opening Remarks at the Media Briefing on Covid 19 - 11 March 2020.
  • WHO, 2023, https://covid19.who.int/, [February 22].
  • Xiong, D., Zhang, L., Watson, G. L., Sundin, P., Bufford, T., Zoller, J. A., Shamshoian, J., Suchard, M. A. and Ramirez, C. M., 2020, Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California, Epidemics, 33, 100418.
  • Xu, Y., Xu, Z., Liu, X., Cai, L., Zheng, H., Huang, Y., Zhou, L., Huang, L., Lin, Y. and Deng, L., 2020, Clinical findings in critically ill patients infected with SARS-CoV-2 in Guangdong Province, China: A multi-center, retrospective, observational study, MedRxiv, 2020.2003. 2003.20030668.
  • Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y. and Zhou, Y., 2020, Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis, International journal of infectious diseases, 94, 91-95.
  • Yupari-Azabache, I., Bardales-Aguirre, L., Rodriguez-Azabache, J., Barros-Sevillano, J. S. and Rodríguez-Diaz, A., 2021, COVID-19 mortality risk factors in hospitalized patients: A logistic regression model, Revista de la Facultad de Medicina Humana, 21 (1), 19-27.
  • Zhou, F., Chen, T. and Lei, B., 2020, Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression, arXiv preprint arXiv:2006.16942.
There are 18 citations in total.

Details

Primary Language English
Subjects Statistical Analysis
Journal Section Research Articles
Authors

Caner Tanış 0000-0003-0090-1661

Publication Date April 24, 2024
Submission Date August 1, 2023
Published in Issue Year 2024 Volume: 50 Issue: 1

Cite

APA Tanış, C. (2024). Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi, 50(1), 1-5. https://doi.org/10.35238/sufefd.1335965
AMA Tanış C. Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model. sufefd. April 2024;50(1):1-5. doi:10.35238/sufefd.1335965
Chicago Tanış, Caner. “Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model”. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi 50, no. 1 (April 2024): 1-5. https://doi.org/10.35238/sufefd.1335965.
EndNote Tanış C (April 1, 2024) Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi 50 1 1–5.
IEEE C. Tanış, “Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model”, sufefd, vol. 50, no. 1, pp. 1–5, 2024, doi: 10.35238/sufefd.1335965.
ISNAD Tanış, Caner. “Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model”. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi 50/1 (April 2024), 1-5. https://doi.org/10.35238/sufefd.1335965.
JAMA Tanış C. Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model. sufefd. 2024;50:1–5.
MLA Tanış, Caner. “Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model”. Selçuk Üniversitesi Fen Fakültesi Fen Dergisi, vol. 50, no. 1, 2024, pp. 1-5, doi:10.35238/sufefd.1335965.
Vancouver Tanış C. Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model. sufefd. 2024;50(1):1-5.
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