Impact of outbreaks caused by respiratory viruses on healthcare and economic systems: A systematic review

Abstract

A pandemic’s socioeconomic disruption can result in deaths due to deprivation, suicide, violence, and trauma, in addition to virus-related consequences. This study seeks to map the scientific literature on the effects of mitigation measures for respiratory virus outbreaks on healthcare and economic systems across countries, as well as the models used to predict those effects. The primary objectives were to identify the main contributions in this field and delineate the major research pathways that can inform a future research agenda. The study uses bibliometric analysis, keyword co-occurrence analysis, and cluster analysis. Keyword linkages were examined to identify possible trends across the retrieved papers. Hierarchical cluster analysis was also applied to categorize related papers into distinct groups. The results facilitated the identification and classification of multiple theoretical perspectives derived from primary research across seven major approaches: (i) economic parameters affected by the COVID-19 crisis; (ii) healthcare crisis management; (iii) predictions of government interventions’ impact on the healthcare system; (iv) impacts of influenza virus in a global economic scenario; (v) general impacts of outbreaks in European and Asia Pacific countries; (vi) operating statistical stability in data analysis; and (vii) statistical trends regarding healthcare in a global economy over a pandemic crisis. Overall, the review synthesizes the main themes in the literature and highlights priority areas related to economic systems, healthcare systems, and predictive modeling. The findings highlight the strong interconnections among economic stability, healthcare system resilience, and public policy, while identifying key health and economic parameters that may inform predictive models assessing the effects of mitigation measures.

Keywords

Bibliometrics / Healthcare system / Economic system / Pandemic’s socioeconomic disruption / COVID-19 / SARS-CoV-2

Funding

The authors are grateful to the Foundation for Science and Technology (FCT; Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). Also, the researcher Kathleen Carvalho is grateful to the FCT (Portugal) for its support of the Ph.D. scholarship.

Conflict of interest

Mihajlo Jakovljevic is the Founding Editor-in-Chief and Joao Paulo Teixeira is an Editorial Board Member of this journal, but were not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

References

[1] Adil M.T., Rahman R., Whitelaw D., Jain V., Al-Taan O., Rashid F., Munasinghe A., & Jambulingam P. (2021). SARS-CoV-2 and the pandemic of COVID-19. Postgraduate Medical Journal, 97(1144), 110-116. https://doi.org/10.1136/postgradmedj-2020-138386

[2] Akram S., Alam M.S., & Shah S. (2024). Significant impact exerted on global economy by implementing pandemi-creactive global-scale public health measures. Global Health Economics and Sustainability, 3(3), 92-100. https://doi.org/10.36922/ghes.4531

[3] Aleta A., Martin-Corral D., Pastore y Piontti A., Ajelli M., Litvinova M., Chinazzi M., Dean N. E., Halloran M. E., Longini Jr I. M., Merler S., Pentland A., Vespignani A., Moro E., & Moreno Y. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 4(9), 964-971. https://doi.org/10.1038/s41562-020-0931-9

[4] Ali A. (2024). Sustainability of specialized healthcare in upper-middle- income economies: Innovations despite constraints. Global Health Economics and Sustainability, 2(3), 2717. https://doi.org/10.36922/ghes.2717

[5] Almehmadi A. (2021). COVID-19 Pandemic Data Predict the Stock Market. Computer Systems Science and Engineering, 36(3), 451-460. https://doi.org/10.32604/CSSE.2021.015309

[6] Amiri A., & Solankallio-Vahteri T. (2020). Analyzing economic feasibility for investing in nursing care: Evidence from panel data analysis in 35 OECD countries. International Journal of

[7] Nursing Sciences, 7(1), 13-20. https://doi.org/10.1016/j.ijnss.2019.06.009

[8] Arabi Y.M., Azoulay E., Al-Dorzi H.M., Phua J., Salluh J., Binnie A., et al. (2021). How the COVID-19 pandemic will change the future of critical care. Intensive care medicine, 47(3), 282-291. https://doi.org/10.1007/s00134-021-06352-y

[9] Ardabili S., Mosavi A., Ghamisi P., Ferdinand F., Varkonyi-Koczy A., Reuter U., Rabczuk T., & Atkinson P. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10). https://doi.org/10.3390/a13100249

[10] Aydın H., Doğan H., & Erdoğan M.O. (2023). Comparison of COVID-GRAM, 4C Mortality, qSOFA, SIRS, NEWS, and MEWS in Predicting Mortality in COVID-19. Medical Journal of Bakirkoy, 19(1), 111-118. https://doi.org/10.4274/BMJ.galenos.2023.2022.7-10

[11] Beutels P., Jia N., Zhou Q.Y., Smith R., Cao W.C., & De Vlas S.J. (2009). The economic impact of SARS in Beijing, China. Tropical Medicine and International Health, 14(SUPPL. 1), 85-91. https://doi.org/10.1111/j.1365-3156.2008.02210.x

[12] Bizuneh M., & Geremew M. (2021). Assessing the Impact of Covid-19 Pandemic on Emerging Market Economies’ (EMEs) Sovereign Bond Risk Premium and Fiscal Solvency. Eastern Economic Journal, 47(4), 519-545. https://doi.org/10.1057/s41302-021-00201-y

[13] Blakely T., Thompson J., Bablani L., Andersen P., Ait Ouakrim D., Carvalho N., Abraham P., Boujaoude M.-A., Katar A., Akpan E., Wilson N., & Stevenson M. (2021). Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns with Health-Adjusted Life-Years and Costs in Victoria, Australia. JAMA Health Forum, 2(7), E211749. https://doi.org/10.1001/jamahealthforum.2021.1749

[14] Boncristiani H.F. (2009). Respiratory Viruses. Encyclopedia of Microbiology, January, 500-518. https://doi.org/10.1016/B978-012373944-5.00314-X

[15] Bonfiglio A., Coderoni S., & Esposti R. (2022). Policy responses to COVID-19 pandemic waves: Cross-region and cross-sector economic impact. Journal of Policy Modeling, 44(2), 252-279. https://doi.org/10.1016/j.jpolmod.2022.03.009

[16] Borghi P.H., Zakordonets O., & Teixeira J.P. (2021). A COVID-19 time series forecasting model based on MLP ANN. Procedia Computer Science, 181(2019), 940-947. https://doi.org/10.1016/j.procs.2021.01.250

[17] Browne C.J., Gulbudak H., & Macdonald J.C. (2022). Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China. Journal of Theoretical Biology, 532(Xx). https://doi.org/10.1016/j.jtbi.2021.110919

[18] Buyukbaşaran T., Karasoy-Can G., & Kucuk H. (2022). Macroeconomic effects of bank lending in an emerging economy: Evidence from Turkey. Economic Modelling, 115, 105946. https://doi.org/10.1016/j.econmod.2022.105946

[19] Carvalho K., Vicente J.P., Jakovljevic M., & Teixeira J.P.R. (2021). Analysis and forecasting incidence, intensive care unit admissions, and projected mortality attributable to covid-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 weeks ahead. Bioengineering, 8(6). https://doi.org/10.3390/bioengineering8060084

[20] Cheteni P., & Mazenda A. (2023). Economic impact of government intervention in response to covid-19 in selected sub-Saharan African countries. Development Southern Africa, 40(2), 406-420. https://doi.org/10.1080/0376835X.2022.2046550

[21] de Oliveira L.S., Gruetzmacher S.B., & Teixeira J.P. (2021). Covid-19 time series prediction. Procedia Computer Science, 181(2019), 973-980. https://doi.org/10.1016/j.procs.2021.01.254

[22] Do T.T., & Pham V.H. (2023). Influence of the Covid-19 Pandemic on Reducing the Income of Workers. Corporate Governance and Organizational Behavior Review, 7(2), 138-146. https://doi.org/10.22495/cgobrv7i2p12

[23] Farah Z., El Naja H. A., Tempia S., Saleh N., Abubakar A., Maison P., & Ghosn N. (2023). Estimation of the influenza-associated respiratory hospitalization burden using sentinel surveillance data, Lebanon, 2015-2020. Influenza and Other Respiratory Viruses, 17(4), 1-8. https://doi.org/10.1111/irv.13138

[24] Fineberg H.V. (2014). Pandemic Preparedness and Response—Lessons from the H1N1 Influenza of 2009. New England Journal of Medicine, 370(14), 1335-1342. https://doi.org/10.1056/nejmra1208802

[25] Gormsen N.J., & Koijen R.S.J. (2020). Coronavirus: Impact on stock prices and growth expectations. Review of Asset Pricing Studies, 10(4), 574-597. https://doi.org/10.1093/rapstu/raaa013

[26] Grey S., & MacAskill A. (2020). Special Report: Johnson listened to his scientists about coronavirus - but they were slow to sound the alarm. Reunters. https://www.reuters.com/article/us-health-coronavirus-britain-path-speciidUSKBN21P1VF

[27] Hashim M.J., Alsuwaidi A.R., & Khan G. (2020). Population risk factors for COVID-19 mortality in 93 countries. Journal of Epidemiology and Global Health, 10(3), 204-208. https://doi.org/10.2991/jegh.k.200721.001

[28] Huseynova A., Mazanova O., Mammadova S., Majidova S., Aslanova A., & Rustamova S. (2022). Analysis of the Relationship between the Economic Confidence Index and Gross Domestic Product Growth in Azerbaijan. WSEAS Transactions on Business and Economics, 19(March), 867-875. https://doi.org/10.37394/23207.2022.19.75

[29] Hysa E., Imeraj E., Feruni N., Panait M., & Vasile V. (2022). COVID-19—A Black Swan for Foreign Direct Investment: Evidence from European Countries. Journal of Risk and Financial Management, 15(4). https://doi.org/10.3390/jrfm15040156

[30] Inoue H., & Todo Y. (2020). The propagation of economic impacts through supply chains: The case of a mega-city lockdown to prevent the spread of COVID-19. PLoS one, 15(9 September), 1-10. https://doi.org/10.1371/journal.pone.0239251

[31] Jakovljevic M., Chang H., Pan J., Guo C., Hui J., Hu H., Grujic D., Li Z., & Shi L. (2023). Successes and challenges of China’s health care reform: a four-decade perspective spanning 1985—2023. Cost Effectiveness and Resource Allocation, 21(1), 59. https://doi.org/10.1186/s12962-023-00461-9

[32] Jakovljevic M., Groot W., & Souliotis K. (2016). Health care financing and affordability in the emerging global markets. Frontiers in Public Health, 4. https://doi.org/10.3389/fpubh.2016.00002

[33] Jakovljevic M., Liu Y., Cerda A., Simonyan M., Correia T., Mariita R.M., et al. (2021). The Global South political economy of health financing and spending landscape history and presence. Journal of Medical Economics, 24(Sup1), 25-33. https://doi.org/10.1080/13696998.2021.2007691

[34] Jakovljevic M.M. (2016). Comparison of historical medical spending patterns among the BRICS and G7. Journal of Medical Economics, 19(1), 70-76. https://doi.org/10.3111/13696998.2015.1093493

[35] Jin H., Li B., & Jakovljevic M. (2022). How China controls the Covid-19 epidemic through public health expenditure and policy? Journal of Medical Economics, 25, 437-449. https://doi.org/10.1080/13696998.2022.2054202

[36] Jin H., Xue J., Yang H., Zhu Z., & Jakovljevic M. (2024). How long has it taken China’s economy to recover from the COVID-19 epidemic. Global Health Economics and Sustainability. https://doi.org/10.36922/ghes.1842

[37] Katris C. (2021). Unemployment and COVID-19 Impact in Greece: A Vector Autoregression (VAR) Data Analysis †. Engineering Proceedings, 5(1). https://doi.org/10.3390/engproc2021005041

[38] Kim K.H., Tandi T.E., Choi J.W., Moon J.M., & Kim M.S. (2016). Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak in South Korea, 2015: epidemiology, characteristics and public health implications. Journal of Hospital Infection, 95(2), 207-213. https://doi.org/10.1016/j.jhin.2016.10.008

[39] Klement R.J., & Walach H. (2022). Identifying factors associated with COVID-19 related deaths during the first wave of the pandemic in Europe. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.922230

[40] Krumel T.P., Goodrich C., & Fiala N. (2023). Labour demand in the time of post-COVID-19. Applied Economics Letters, 30(3), 343-348. https://doi.org/10.1080/13504851.2021.1985067

[41] Lai D.-H., Le T.-H., & Tran-Nam B. (2024). Effectiveness of the lockdown policy in Vi-etnam during the COVID-19 pandemic. Global Health Economics and Sustainability, 2(4), 3423. https://doi.org/10.36922/ghes.3423

[42] Lau K., Dorigatti I., Miraldo M., & Hauck K. (2021). SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals. International Journal of Infectious Diseases, 105, 161-171. https://doi.org/10.1016/j.ijid.2021.01.070

[43] Leung V. K. Y., Wong J. Y., Barnes R., Kelso J., Milne G. J., Blyth C. C., Cowling B. J., Moore H. C., & Sullivan S. G. (2021). Excess respiratory mortality and hospitalizations associated with influenza in Australia, 2007-2015. International Journal of Epidemiology, 51(2), 458-467. https://doi.org/10.1093/ije/dyab138

[44] Lilleri D., Zavaglio F., Gabanti E., Gerna G., & Arbustini E. (2020). Analysis of the SARS-CoV-2 epidemic in Italy: The role of local and interventional factors in the control of the epidemic. PLoS ONE, 15(11), 1-12. https://doi.org/10.1371/journal.pone.0242305

[45] Mashud A.H. M., Hasan M.R., Daryanto Y., & Wee H.M. (2021). A resilient hybrid payment supply chain inventory model for post Covid-19 recovery. Computers and Industrial Engineering, 157, 107249. https://doi.org/10.1016/j.cie.2021.107249

[46] McCombs A., & Kadelka C. (2020). A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies. PLoS Computational Biology, 16(10), 1-18. https://doi.org/10.1371/journal.pcbi.1008388

[47] Mitze T., & Makkonen T. (2020). Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19. Technological Forecasting and Social Change. 2023; 186:122073. https://doi.org/10.1016/j.techfore.2022.122073

[48] Mustafa H., Ahmed F., Zainol W.W., & Enh A.M. (2021). Forecasting the impact of gross domestic product (Gdp) on international tourist arrivals to langkawi, malaysia: A postcovid-19 future. Sustainability (Switzerland), 13(23). https://doi.org/10.3390/su132313372

[49] Navarro Romero E. del C., Gelves Alarcon O.M., & Garcia Corrales N. (2021). Correlational analysis between the economics, socio-demographic indices and statistics of contagion due to Covid-19, applying the Clustering methodology in countries of America. Inge CuC, 17(1), 285-302. https://doi.org/10.17981/ingecuc.17.1.2021.21

[50] Nguyen P.-H., Tsai J.-F., Kayral I.E., & Lin M.-H. (2021). Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam. Sustainability, 13(14), 7879. https://doi.org/10.3390/su13147879

[51] Nguyen L.Q., Fernandes P.O., & Teixeira J.P. (2022). Analyzing and Forecasting Tourism Demand in Vietnam with Artificial Neural Networks. 36-50. https://doi.org/10.3390/forecast4010003

[52] Pang X., Zhu Z., Guo F.X.J., Gong X., Liu D., Liu Z., Chin D.P., Feikin D.R. (2003). Evaluation of control measures implemented in the severe acute respiratory syndrome outbreak in Beijing, 2003. Infectious Diseases in Clinical Practice, 290(24), 3215-3221. https://doi.org/10.1001/jama.290.24.3215

[53] Petrou S., & Jakovljevic M. (2024). Reimagining the relationship between economics and health-WHO ‘Health for all’provisions. Cost Effectiveness and Resource Allocation, 22(1), 5. https://doi.org/10.1186/s12962-024-00512-9

[54] Pollitzer, R. (1951). Plague studies. 1. A summary of the history and survey of the present distribution of the disease. Bulletin of the World Health Organization, 4(4), 475-533.

[55] Rahmani, A.M., & Hosseini Mirmahaleh, S.Y. (2022). An Intelligent Algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn. Computational Economics, 63(3), 1001-1020. https://doi.org/10.1007/s10614-022-10332-9

[56] Reissl S., Caiani A., Lamperti F., Guerini M., Vanni F., Fagiolo G., Ferraresi T., Ghezzi L., Napoletano M., & Roventini A. (2022). Assessing the Economic Impact of Lockdowns in Italy: A Computational Input-Output Approach. Industrial and Corporate Change, 31(2), 358-409. https://doi.org/10.1093/icc/dtac003

[57] Rostan P., & Rostan A. (2022). Assessing the Resilience of Uk’S Economy After the Covid-19 Pandemic and Brexit. Online Journal Modelling the New Europe, 40, 47-77. https://doi.org/10.24193/OJMNE.2022.40.03

[58] Sadovnichiy V.A., Akaev A.A., Zvyagintsev A.I., & Sarygulov A.I. (2022). Mathematical Modeling of Overcoming the COVID-19 Pandemic and Restoring Economic Growth. Doklady Mathematics, 106(1), 230-235. https://doi.org/10.1134/S1064562422040160

[59] Sahin U., Muik A., Derhovanessian E., Vogler I., Kranz L. M., Vormehr M., Baum A., Pascal K., Quandt J., Maurus D., Brachtendorf S., Lorks V., Sikorski J., Hilker R., Becker D., Eller A.-K., Grutzner J., Boesler C., Rosenbaum C., … Tureci O. (2021). Publisher Correction: COVID 19 vaccine BNT162b1 elicits human antibody and TH1 T cell responses. Nature, 590(7844), E17-E17. https://doi.org/10.1038/s41586-020-03102-w

[60] Santosa P.B., Pangestuti I.R. D., Wahyudi S., & Muharam H. (2023). Dividend policy in Indonesian banking sector during COVID-19 pandemic period. Cogent Social Sciences, 9(2). https://doi.org/10.1080/23311886.2023.2272657

[61] Sharma S., Bansal M., & Saxena A.K. (2022). Forecasting of GDP (Gross Domestic Product) per Capita Using (ARIMA) Data-Driven Intelligent Time Series Predicting Approach. 2022 International Conference on Sustainable Islamic Business and Finance, SIBF 2022, 85-90. https://doi.org/10.1109/SIBF56821.2022.9939928

[62] Shi L., Khan Y.A., & Tian M.W. (2022). COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach. PLoS ONE, 17(12 December), 1-17. https://doi.org/10.1371/journal.pone.0275422

[63] Shu Y., & McCauley J. (2017). GISAID: Global initiative on sharing all influenza data - from vision to reality. Eurosurveillance, 22(13), 2-4. https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494

[64] Simonsen L., Spreeuwenberg P., Lustig R., Taylor R. J., Fleming D. M., Kroneman M., Van Kerkhove M. D., Mounts A. W., & Paget W. J. (2013). Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study. PLoS Medicine, 10(11). https://doi.org/10.1371/journal.pmed.1001558

[65] Singh K., Chander Pushap A., Kaur G., Bharti S., Jhon A., Sudershan S., Ahmed Dar F., Ahmad Sheikh B., Bashir M., Ahmad Najar S., & Sudershan A. (2024). COVID-19 changed our world: A systematic review. Global Health Economics and Sustainability, 3(1), 38-63. https://doi.org/10.36922/ghes.3992

[66] Smith R.D., & Keogh-Brown M.R. (2013). Macroeconomic impact of pandemic influenza and associated policies in Thailand, South Africa and Uganda. Influenza and Other Respiratory Viruses, 7(SUPPL. 2), 64-71. https://doi.org/10.1111/irv.12083

[67] Szanyi J., Wilson T., Howe S., Zeng J., Andrabi H., Rossiter S., & Blakely T. (2023). Epidemiologic and economic modelling of optimal COVID-19 policy: public health and social measures, masks and vaccines in Victoria, Australia. The Lancet Regional Health - Western Pacific, 32, 100675. https://doi.org/10.1016/j.lanwpc.2022.100675

[68] Tamesberger D., & Bacher J. (2020). COVID-19 Crisis: How to Avoid a ‘Lost Generation.’ Intereconomics, 55(4), 232-238. https://doi.org/10.1007/s10272-020-0908-y

[69] Towers S., & Chowell G. (2012). Impact of weekday social contact patterns on the modeling of influenza transmission, and determination of the influenza latent period. Journal of Theoretical Biology, 312, 87-95. https://doi.org/10.1016/j.jtbi.2012.07.023

[70] van der Schans, S., Schottler M.H., van der Schans, J., Connolly M.P., Postma M.J., & Boersma C. (2023). Investing in the Prevention of Communicable Disease Outbreaks: Fiscal Health Modelling—The Tool of Choice for Assessing Public Finance Sustainability. Vaccines, 11(4), 823. https://doi.org/10.3390/vaccines11040823

[71] Vaya E., Garcia J.R., Surinach J., & Pons E. (2023). Effects of the COVID-19 tourism crisis on the Spanish economy. Tourism Economics, 0(0), 1-18. https://doi.org/10.1177/13548166231185899

[72] Verma M., & Naveen B.R. (2021). COVID-19 Impact on Buying Behaviour. Vikalpa, 46(1), 27-40. https://doi.org/10.1177/02560909211018885

[73] Verma P., Dumka A., Bhardwaj A., Ashok A., Kestwal M.C., & Kumar P. (2021). A Statistical Analysis of Impact of COVID19 on the Global Economy and Stock Index Returns. SN Computer Science, 2(1), 1-13. https://doi.org/10.1007/s42979-020-00410-w

[74] Wahyono H., Narmaditya B.S., Wibowo A., & Kustiandi J. (2021). Irrationality and economic morality of SMEs’ behavior during the Covid-19 pandemic: lesson from Indonesia. Heliyon, 7(7), e07400. https://doi.org/10.1016/j.heliyon.2021.e07400

[75] Wang Y. (2024). Income-related inequality in health outcomes among older individuals in China: A measurement and decomposition analysis. Global Health Economics and Sus-Tainability, 2(1), 2243. https://doi.org/10.36922/ghes.2243

[76] Wichitaksorn N. (2022). Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach. Journal of Asian Economics, 78, 101421. https://doi.org/10.1016/j.asieco.2021.101421

[77] Wu W., Zhang P., Zhu D., Jiang X., & Jakovljevic M. (2022). Environmental Pollution Liability Insurance of Health Risk and Corporate Environmental Performance: Evidence From China. Frontiers in Public Health, 10, 1-13. https://doi.org/10.3389/fpubh.2022.897386

[78] Yang J.D.X. (2023). Cross-sector co-movements and policy impact in the COVID-19 stock market: A dynamic factor approach. Global Finance Journal, 56, 100772. https://doi.org/10.1016/j.gfj.2022.100772

[79] Yen M.-Y., Chiu, A. W.-H., Schwartz J., King C.-C., Lin Y. E., Chang S.-C., Armstrong D., & Hsueh P.-R. (2014). From SARS in 2003 to H1N1 in 2009: lessons learned from Taiwan in preparation for the next pandemic. Journal of Hospital Infection, 87(4), 185-193. https://doi.org/10.1016/j.jhin.2014.05.005

[80] Yamacli D.S., & Yamacli S. (2023). Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods. Heliyon, 9(1), e12796. https://doi.org/10.1016/j.heliyon.2023.e12796

[81] Zhuang X. (2020). Financial Modeling Analysis of the Impact of Consumer Coupons on Economic Recovery after the Pandemic. 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID), 601-607. https://doi.org/10.1109/msieid52046.2020.00119