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Recent Advances in Computer Science and Communications


ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

A Novel Work on Analyzing STRESS and Depression level of Indian Population During COVID-19

Author(s): Amit Kumar Gupta*, Priya Mathur, Shruti Bijawat and Abhishek Dadhich

Volume 15, Issue 6, 2022

Published on: 22 October, 2020

Article ID: e210322187104 Pages: 10

DOI: 10.2174/2666255813999201022113918

Price: $65


Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the prevailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and depression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease.

Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression.

Results: Women, in general, were more stressed and depressed than men . Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and female students. The overall analysis showed that the people of India were stressed and depressed at “Serve” level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as well as their salary and aged people were depressed due to their health concerns in COVID-19 disaster .

Conclusion: The researchers conducted an analysis of data based on DASS-21 parameters defined for anxiety, depression, and stress at the global level. By the analysis defined in section 5, researchers concluded that the people of India are more stressed and depressed at "Serve" level due to COVID-19.

Keywords: COVID-19, depression, stress, mental state, DASS-21, Machine Learning, Multiple Linear regressions.

Graphical Abstract
Olfson, M.; Druss, B.G.; Marcus, S.C. Trends in mental health care among children and adolescents. N. Engl. J. Med., 2015, 372(21), 2029-2038.
[] [PMID: 25992747]
Whiteford, H.A.; Degenhardt, L.; Rehm, J.; Baxter, A.J.; Ferrari, A.J.; Erskine, H.E.; Charlson, F.J.; Norman, R.E.; Flaxman, A.D.; Johns, N.; Burstein, R.; Murray, C.J.; Vos, T. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet, 2013, 382(9904), 1575-1586.
[] [PMID: 23993280]
Polanczyk, G.V.; Salum, G.A.; Sugaya, L.S.; Caye, A.; Rohde, L.A. Annual research review: A meta-analysis of the worldwide preva-lence of mental disorders in children and adolescents. J. Child Psychol. Psychiatry, 2015, 56(3), 345-365.
[] [PMID: 25649325]
Twenge, J.M. period and birth cohort differences in depressive symptoms in us, 1982–2013. Soc. Indic. Res., 2015, 121(2), 437-454.
Home | Ministry of Health and Family Welfare | GOI
Garcia-Ceja, E.; Riegler, M.; Nordgreen, T.; Jakobsen, P.; Ketil, J. A machine learning approach to identifying objective biomarkers of anxiety and stress,
Garcia-Ceja, E.; Riegler, M.; Nordgreen, T.; Jakobsen, P.; Ketil, J. Oedegaard Jim Tørresen: Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive Mobile Comput., 2018, 51, 1-26.
U SRINIVASULU REDDY. Machine Learning Techniques for Stress Prediction in Working Employees IEEE International Conference on Computational Intelligence and Computing Research, 2018.
Krishnakumar, B.; Rana, S. COVID 19 in INDIA: Strategies to combat from combination threat of life and livelihood. J. Microbiol. Immunol. Infect., 2020, 53(3), 389-391.
[] [PMID: 32253143]
CuiyanWang, Riyu Pan, Xiaoyang Wan, Yilin Tan, Linkang Xu, S.Ho Cyrus, and C. Ho. Roger, “Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019Coronavirus Disease (COVID-19) Epidemic among the General Population in Chi-na”. Int. J. Environ. Res. Public Health, 2020, 17, 1729.
Rubin, G.J.; Potts, H.W.W.; Michie, S. The impact of communications about swine flu (influenza A H1N1v) on public responses to the outbreak: results from 36 national telephone surveys in the UK. Health Technol. Assess., 2010, 14(34), 183-266.
[] [PMID: 20630124]
Ho, C.S.H.; Tan, E.L.Y.; Ho, R.C.M.; Chiu, M.Y.L. Relationship of Anxiety and Depression with Respiratory Symptoms: Comparison between Depressed and Non-Depressed Smokers in Singapore. Int. J. Environ. Res. Public Health, 2019, 16(1), 163.
[] [PMID: 30626156]
McAlonan, G.M.; Lee, A.M.; Cheung, V.; Cheung, C.; Tsang, K.W.; Sham, P.C.; Chua, S.E.; Wong, J.G. Immediate and sustained psycho-logical impact of an emerging infectious disease outbreak on health care workers. Can. J. Psychiatry, 2007, 52(4), 241-247.
[] [PMID: 17500305]
Patel, A.; Jernigan, D.B. 2019-nCoV CDC Response Team, “Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak - United States, December 31, 2019-February 4, 2020”. MMWR Morb. Mortal. Wkly. Rep., 2020, 69(5), 140-146.
[] [PMID: 32027631]
Xiang, Y-T.; Yang, Y.; Li, W.; Zhang, L.; Zhang, Q.; Cheung, T.; Ng, C.H. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry, 2020, 7(3), 228-229.
[] [PMID: 32032543]
Wu, J.; Guo, S.; Huang, H.; Liu, W.; Xiang, Y. Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs, and Perspectives. IEEE Commun. Surv. and Tutor., 2018, 20, 2389-2406.
Wu, J.; Guo, S.; Li, J.; Zeng, D. Big Data Meet Green Challenges: Greening Big Data; IEEE Syst. J. Vol. 10, 2016.
Quek, T.C.; Ho, C.S.; Choo, C.C.; Nguyen, L.H.; Tran, B.X.; Ho, R.C. Misophonia in Singaporean Psychiatric Patients: A Cross-Sectional Study. Int. J. Environ. Res. Public Health, 2018, 15(7), 1410.
[] [PMID: 29973546]
Le, T.A.; Le, M.Q.T.; Dang, A.D.; Dang, A.K.; Nguyen, C.T.; Pham, H.Q.; Vu, G.T.; Hoang, C.L.; Tran, T.T.; Vuong, Q.H.; Tran, T.H.; Tran, B.X.; Latkin, C.A.; Ho, C.S.H.; Ho, R.C.M. Multi-level predictors of psychological problems among methadone maintenance treatment pa-tients in difference types of settings in Vietnam. Subst. Abuse Treat. Prev. Policy, 2019, 14(1), 39.
[] [PMID: 31533764]
Nishiura, H.; Jung, S.M.; Linton, N.M.; Kinoshita, R.; Yang, Y.; Hayashi, K.; Kobayashi, T.; Yuan, B.; Akhmetzhanov, A.R. The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020. J. Clin. Med., 2020, 9(2), 330.
[] [PMID: 31991628]
Mahase, E. China coronavirus: WHO declares international emergency as death toll exceeds 200. BMJ Clin. Res. Ed., 2020, 368, 408.
Lai, C-C.; Liu, Y.H.; Wang, C-Y.; Wang, Y-H.; Hsueh, S-C.; Yen, M-Y.; Ko, W.C.; Hsueh, P.R. Asymptomatic carrier state, acute respira-tory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Facts and myths. J. Microbiol. Immunol. Infect., 2020, 53(3), 404-412.
[] [PMID: 32173241]

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