# COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

## Authors

• David Opeoluwa Oyewola Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria
• Emmanuel Gbenga Dada Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
• Juliana Ngozi ndunagu Department of Computer Sciences, National Open University of Nigeria, Nigeria
• Terrang Abubakar Umar 5Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria
• Akinwunmi S.A 5Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria

## Keywords:

COVID-19 Structural Equation Modelling, Latent variables, Random forest, Boosting.

## Abstract

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

Dimensions

E.J. Williamson, A.J. Walker, K. Bhaskaran, S. Bacon, C. Bates, C.E. Morton, H.J. Curtis, A. Mehrkar, D. Evans, P. Inglesby, J. Cockburn, “Factors associated with COVID-19-related death using Open Safely”, Nature, 584 (2020) 430 DOI: https://doi.org/10.1038/s41586-020-2521-4

X. Yewwei, W. Zaisheng, L. Huipeng, M. Gifty, W. Dan, T. Weiming, “Epidemiologic, clinical and laboratory findings of the COVID-19 in the current pandemic: systematic review and meta-analysis”, BMC Infectious Diseases (2020) 1.

F. Zakaria, A. F. Filali, “The COVID-19: macroeconomics scenario and role of containment in Morocco”, One Health, 10 (2020) 100152. DOI: https://doi.org/10.1016/j.onehlt.2020.100152

Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, R. Ren, K.S. Leung, E.H. Lau, J.Y. Wong, X. Xing, “Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia”, N. Engl. J. Med, 382 (2020) 1199. DOI: https://doi.org/10.1056/NEJMoa2001316

S. Roush, H. Fast, C.E. Miner, H. Vins, L. Baldy, R. McNall, S. Kang, V. Vund, “National Center for Immunization and Respiratory Diseases (NCIRD) Support for Modernization of the Nationally Notifiable Diseases Surveillance System (NNDSS) to Strengthen Public Health Surveillance Infrastructure in the US. In 2019”, CSTE Annual Conference. CSTE

S.A. Ekanem, E.P.K. Imarenezor, C.P. Kolisah, “An Essencist Evaluation of Socio-Economic Impacts of Coronavirus Disease (COVID-19) Pandemic in Nigeria”. Mediterranean Journal of Social Sciences 11(2020) 70. DOI: https://doi.org/10.36941/mjss-2020-0057

A. Obioma, A.A. Reuben, A.B. Elekwachi, “Potential Impact of COVID-19 Pandemic on the Socio-Economic Situations in Nigeria: A Huge Public Health Risk of unprecedented Concern”, J Qual Healthcare Eco., 3 (2020) 000175.

L.L. Ren, Y.M. Wang, Z.Q. Wu, Z.C. Xiang, L. Guo, T. Xu, Y.Z. Jiang, Y. Xiong, Y.J. Li, X.W. Li, H. Li, “Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study”, Chinese medical journal, 133 (2020) 1015-1024 DOI: https://doi.org/10.1097/CM9.0000000000000722

WHO. Novel Coronavirus–China: https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/. Accessed: 20 October, 2020.

National Center for Immunization and Respiratory Diseases (NCIRD) DoVD. Coronavirus Disease 2019 (COVID-19) Situation Summary: Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-nCoV/summary.html. Accessed: 15 November, 2020.

From pandemic to poverty: Nigeria’s future with COVID-19. (May 2020). Nairametrics. Retrieved from https://nairametrics.com/2020/05/17/from-pandemic-to-poverty-nigerias-future-with-COVID-19/. Accessed: 16 November, 2020.

Coronavirus: https://www.worldometers.info/coronavirus/ Accessed: 27, October 2020.

K.B. Ajide, R.L. Ibrahim, O.Y. Alimi, “Estimating the impacts of lockdown on COVID-19 cases in Nigeria. Transportation Research Interdisciplinary Perspectives” 7 (2020) 100217. DOI: https://doi.org/10.1016/j.trip.2020.100217

D. O. Oyewola, A. F. Augustine, E. G. Dada, A. Ibrahim, “Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolutional Neural Network”, Journal of Robotics and Control, 2 (2020) 103-109.

B.N. Ashraf, “Stock markets’ reaction to COVID-19: Cases or fatalities?”, Research in International Business and Finance, 54 (2020) 101249. DOI: https://doi.org/10.1016/j.ribaf.2020.101249

E. Mogaji, “Impact of COVID-19 on transportation in Lagos, Nigeria”, Transportation Research Interdisciplinary Perspectives 6 (2020) 100154. DOI: https://doi.org/10.1016/j.trip.2020.100154

S. Ghosal, S. Sengupta, M. Majumder, B. Sinha, “Linear Regression Analysis to Predict the number of deaths in India due to SARS-COV-2 at 6 weeks from day 0 to 100 cases March 14th 2020, Diabetes & Metabolic Syndrome”, Clinical Research & Reviews, 14 (2020) 311. DOI: https://doi.org/10.1016/j.dsx.2020.03.017

K. Ayinde, F. A. Lukman, I. Rauf, O. O. Alabi, C. E. Okon,

O. E. Ayinde, “Modeling Nigerian COVID-19 cases: A comparative analysis of models and estimators”, Chaos Solitions and Fractals, 138 (2020) 109911. DOI: https://doi.org/10.1016/j.chaos.2020.109911

A. Sharif, C. Aloui, L. Yarovaya, “COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach”, International Review of Financial Analysis, 70 (2020) 101496. DOI: https://doi.org/10.1016/j.irfa.2020.101496

J. Wang, W. Shao, J. Kim, “Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures”, Chaos, Solitons and Fractals, 136 (2020) 109896. DOI: https://doi.org/10.1016/j.chaos.2020.109896

F. Rustam, A. A. Reshi, A. Mehmood, S. Ullah, B. W. On, W. Aslam, G. S. Choi, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”, IEEE, 8 (2020) 101489. DOI: https://doi.org/10.1109/ACCESS.2020.2997311

L. J. Muhammad, M. M. Islam, S. S. Usman, S. I. Ayon, “Predictive Data Mining Models for Novel Coronavirus (COVID 19) Infected Patients’ Recovery”, SN Computer Science (2020) 1. DOI: https://doi.org/10.1007/s42979-020-00216-w

A. Spad, F. A. Tucci, A. Ummarino, P. P. Ciavarella et al., “Structural equation modeling to shed light on the controversial role of climate on the spread of SARS CoV 2”, Scientific Reports, 11 (2020) 8358. DOI: https://doi.org/10.1038/s41598-021-87113-1

S. G. Purnama, D. Susanna, “Attitude to COVID-19 Prevention with Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling” Front. Public Health, 8 (2020) 570394. doi: 10.3389/fpubh.2020.570394. DOI: https://doi.org/10.3389/fpubh.2020.570394

S. Šuri, K. Martinsone, V. Perepjolkina, J. Kolesnikova, U. Vainik, A. Ruža, J. Vrublevska, D. Smirnova, K.N. Fountoulakis, E. Rancans, “Factors Related to COVID-19 Preventive Behaviors: A Structural Equation Model”, Front. Psychol., 12 (2021) 676521. doi: 10.3389/fpsyg.2021.676521. DOI: https://doi.org/10.3389/fpsyg.2021.676521

S. Pai, V. Patil, R. Kamath, M. Mahendra, D.K. Singhal, V. Bhat, “Work-life balance

amongst dental professionals during the COVID-19 pandemic—A structural equation modelling approach”, PLoS ONE, 16 (2021): e0256663. https://doi.org/10.1371/journal.pone.0256663 DOI: https://doi.org/10.1371/journal.pone.0256663

A. Franzen, F. Wohner, “Coronavirus risk perception and compliance with social

distancing measures in a sample of young adults: Evidence from Switzerland”, PLoS ONE, 16 (2021):e0247447. https://doi.org/10.1371/journal.pone.0247447 DOI: https://doi.org/10.1371/journal.pone.0247447

Kaggle: https://www.kaggle.com/kimjihoo/coronavirusdataset. Accessed: 18, September,

Yahoo Finance: https://finance.yahoo.com/. Accessed: 19, September, 2020.

Y. Liping, C. Yuqing, P. Yuntao, W. Yishan, “Research on the evaluation of academic journals based on structural equation modeling, Journal of Informetrics” 3 (2019) 304. DOI: https://doi.org/10.1016/j.joi.2009.04.002

S. Wright, “Correlation and Causation”, Journal of Agricultural Research 20 (1921) 557.

S. Wright. S (1934), “The method of path coefficients”, Annals of Mathematical Statistics DOI: https://doi.org/10.1214/aoms/1177732676

(1934) 161

S. Kocakaya, F. Kocakaya, “A Structural Equation Modeling on Factors of How Experienced Teachers Affects the Students Science and Mathematics Achievements”, Education Research International, (2014) 1-8. DOI: https://doi.org/10.1155/2014/490371

J.H. Hair, R. L. Tatham, R. E. Anderson, “Multivariate Data Analysis”, Prentice Hall International, New York, NY, USA, 5th edition, 1998.

COVID-19 Risk Factors: https://www.cdc.gov/coronavirus/2019-ncov/COVID-data/investigations-discovery/assessing-risk-factors.html. Accessed: 23 October, 2020.

D. O. Oyewola, A. F. Augustine, E. G. Dada, A. Ibrahim, “Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolutional Neural Network”, Journal of Robotics and Control (JRC), 2 (2020) 103. DOI: https://doi.org/10.18196/jrc.2261

D. O. Oyewola, E. G. Dada, O. T. Omotehinwa, I.A. Ibrahim, “Comparative Analysis of Linear, Non Linear and Ensemble MachineLearning Algorithms for Credit Worthiness of Consumers”, Computational Intelligence & Wireless Sensor Networks, 1 (2019) 1.

J. H. Friedman, “Stochastic gradient boosting”, Computational Statistics & Data Analysis, 38 (2002) 367–378. DOI: https://doi.org/10.1016/S0167-9473(01)00065-2

Y. Shin, “Application of Stochastic Gradient Boosting Approach to Early Prediction of Safety Accidents at Construction Site”, Advances in Civil Engineering (2019) 1 DOI: https://doi.org/10.1155/2019/1574297

S. Kim, J. Choi, “An SVM-based high-quality article classifier for systematic reviews”, Journal of Biomedical Informatics 47 (2014) 153 DOI: https://doi.org/10.1016/j.jbi.2013.10.005

R. Katuwal, P.N Suganthan, L. Zhang, “Heterogeneous Oblique Random Forest”, Pattern Recognition, 99 (2019) 107078. DOI: https://doi.org/10.1016/j.patcog.2019.107078

S. Sivakumar, S. Venkataraman, R. Selvaraj, “Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree”, Indian Journal of Science and Technology, 9 (2016) 1 DOI: https://doi.org/10.17485/ijst/2016/v9i4/87032

2021-11-29

## How to Cite

David Opeoluwa Oyewola, Dada, E. G. ., Ndunagu , J. N., Abubakar Umar, T. ., & S.A, A. (2021). COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches. Journal of the Nigerian Society of Physical Sciences, 3(4), 395–405. https://doi.org/10.46481/jnsps.2021.173

## Section

Original Research