Dynamic indexing and clustering of government strategies to mitigate Covid-19


Objective: The objective of the article is to identify the reference group of countries with similar Covid strategies and other groups with their performance success, and to construct a composite Covid Mitigation Index for comparative purposes, thus, implying how to redesign the strategic policies.

Research Design & Methods: Gaussian Mixture Modelling and Factor Analysis: the main design is quantitative, using Gaussian Mixture Modelling to find the optimal number of country clusters, and Factor Analysis with Principal Axis Factoring (FA-PAF) to build a composite index of governmental policies. Data includes eight mitigation policy variables and three supporting economic policy variables. Data are aggregated to form three periods and the cluster changes are identified by Gaussian Mixture Modelling. Then, the Covid Mitigation Index (CMI) is constructed by FA-PAF to obtain a comparative measure over the periods and the country clusters. The results were obtained by means of R studio and SPSS.

Findings: The dynamic clustering leads to a decreasing number of clusters from nine clusters in the first period (Janunary-February 2020), four clusters in the second period (March-April 2020), and two clusters in the third period (May-June 2020). In the first period, China (with CMI=48) took serious actions forming its own cluster, while 11 other countries (with CMI>10), e.g., early affected European countries such as Italy and Spain and large Asian countries such India and Indonesia, took moderate actions. In the second period all cluster averages were greater than China’s in the first period, i.e., most world countries were dedicated to fight Covid-19. In Europe, Italy, San Marino and France showed the highest CMI values, similarly to Iraq and Palestine in the Middle East, Peru and Honduras in the Latin America, and China, India and Indonesia in Asia. In the third period, cluster averages showed even tighter policies even though 42 countries had lower CMI values than previously.

Implications & Recommendations: The approach provided a big picture for decision makers both in business and in governments. The key idea was to reveal reference groups of countries which help governmental actors to design and adapt their strategies over time by learning by their own experience and the results of the better performing clusters. It was suggested that a multi-criteria approach accounting for individual government’s preferences over health and economy is used along with the presented approach.

Contribution & Value Added: Clustering with Gaussian Mixture Models and factor analysis based on Principal Axis Factoring for composite-index building were used. The methods are well-established, but they were applied in a novel way dynamically over time and for the composite CMI. CMI was built on two factors which identified the structure of mitigation policies and economic policies. The development of governmental polices over the first cycle of Covid-19 pandemic was described.


clustering; composite index; pandemic; public policy; Covid-19 mitigation

Andersen, N., Bramness, J.G., & Lund, I.O. (2020). The emerging COVID-19 research: dynamic and regularly up-dated science mapsand analyses. BMC Medical Informatics and Decision Making, 20(1), 1-7. https://doi.org/10.1186/s12911-020-01321-9

Androniceanu, A. (2020). Major structural changes in the EU policies due to the problems and risks caused by COVID-19. Administratie si Management Public, 34, 137-149. https://doi.org/10.24818/amp/2020.34-08

Androniceanu, A., Kinnunen, J., & Georgescu, I. (2020). E-Government clusters in the EU based on the Gaussian Mixture Models. Administratie si Management Public, 35, 6-20. https://doi.org /10.24818/amp/2020.35-01

Androniceanu, A., & Marton, D.- M., (2021). The psychosocial impact of the Romanian government measures on the population during the COVID-19 pandemic. Central European Public Administration Review, 19(1), 7–32.

Androniceanu, A.-M., Georgescu, I., Dobrin, C., & Dragulanescu I.V. (2020a). Multifactorial components analysis of the renewable energy sector in the OECD countries and managerial implications. Polish Journal of Man-agement Studies, 22 (2), 36-49. https://doi.org/10.17512/pjms.2020.22.2.03

Androniceanu, A.-M., Georgescu, I., Tvaronavičiene, M., & Androniceanu, A.(2020b). Canonical Correlation Analysis and a New Composite Index on Digitalization and Labor Force in the Context of the Industrial Revo-lution 4.0. Sustainability, 12(17), 6812. https://doi.org/10.3390/su12176812

Biernacki, G., Celeux, G., & Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719–725. https://doi.org/10.1109/34.865189

Bouveyron, C., Celeux, G., Murphy, T.B., & Raftery, A.E. (2019). Model-based clustering and classification for data science. Cambridge University Press.

Carrasco Sierra, A., Cobos Flores, M.J., Fuentes Duarte, B., & Hernández Comi, B.I. (2020). Successful Manage-ment System by a Metalworking Mexican Company During Covid-19 Situation. Analysis Through a New Index (Case Study). International Journal of Entrepreneurial Knowledge, 8(2), 42-55. https://doi.org/10.37335 /ijek.v8i2.116

Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781-93. https://doi.org/10.1016/0031-3203(94)00125-6

Cheng, C., Barceló, J., Hartnett, A.S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 government response event dataset (CoronaNet v. 1.0). Nature Human Behaviour, 4(7), 756-768. https://doi.org/10.1038/s41562-020-0909-7

Davulis, T., Gasparėnienė, L., & Raistenskis, E. (2021). Assessment of the situation concerning psychological sup-port to the public and business in the extreme conditions: case of Covid-19. Entrepreneurship and Sustaina-bility Issues, 8(3), 308-322. https://doi.org/10.9770/jesi.2021.8.3(19)

Fabrigar, L.R., Wegener, D.T., Maccallum, R.C., & Strahan, E.J. (1999). The use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272

Fetzer, T.R., Witte, M., Hensel, L., Jachimowicz, J., Haushofer, J., Ivchenko, A., Caria, S., Reutskaya, E., Roth, C.P., Fiorin, S., Gómez, M., Kraft-Todd, G., Götz, F.M., & Yoeli, E. (2020). Global behaviors and perceptions at the onset of the COVID-19 pandemic (No. w27082). National Bureau of Economic Research.

Hale, T., Petherick, A., Phillips, T., & Webster, S. (2020a). Variation in government responses to COVID-19. Blavatnik School of Government Working Paper, 31, 2020-11.

Hale, T., Webster, S., Petherick, A., Phillips, T., & Kira, B. (2020b). Stringency Index (OXBS). Oxford COVID-19 Government Response Tracker. Blavatnik School of Government Working Paper. Retrieved from https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker on February 20, 2021.

Haleem, A., Javaid, M., & Vaishya, R. (2020). Effects of COVID-19 pandemic in daily life. Current Medicine Research and Practice, 78-79. https://doi.org/10.1016 /j.cmrp.2020.03.011

James, N., & Menzies, M. (2020). Cluster-based dual evolution for multivariate time series: Analyzing COVID-19. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(6), 061108. https://doi.org/10.1063/5.0013156

Joliffe, I.T. (2002). Principal component analysis. Springer. 2nd edition

Kinnunen, J., & Georgescu, I. (2020). Disruptive pandemic as a driver towards digital coaching in OECD countries. Revista Românească pentru Educaţie Multidimensională, 12(2Sup1), 55-61. https://doi.org/10.18662/rrem/12.2Sup1/289

Kinnunen, J., Georgescu, I., & Androniceanu, A.-M. (2020). Evaluating governmental responses to Covid-19 and the implications for tourism industry. In Proceedings of 14th International Management Conference: Manag-ing Sustainable Organizations, Bucharest, Romania, (pp. 585-594). https://doi.org/10.24818/IMC/2020/03.11

Kosach, I., Duka, A., Starchenko, G., Myhaylovska, O., & Zhavoronok, A. (2020). Socioeconomic viability of public management in the context of European integration processes. Administratie si Management Public, 35, 139-152. https://doi.org/10.24818/amp/2020.35-09

Marona, B., & Tomal, M. (2020). The COVID-19 pandemic impact upon housing brokers’ workflow and their cli-ents’ attitude: Real estate market in Krakow. Entrepreneurial Business and Economics Review, 8(4), 221-232. https://doi.org/10.15678/EBER.2020.080412

McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc.

Mollenkopf, D.A., Ozanne, L.K., & Stolze, H.J. (2020). A transformative supply chain response to COVID-19. Jour-nal of Service Management, ahead-of-print. https://doi.org/10.1108/JOSM-05-2020-0143

Moyo, N. (2020). Antecendents of employee disengagement amid COVID-19 pandemic. Polish Journal of Man-agement Studies, 22(1), 323-334. https://doi.org/10.17512/pjms.2020.22.1.21

Nowiński, W., Haddoud, M., Wach, K., & Schaefer, R. (2020). Perceived public support and entrepreneurship attitudes: A little reciprocity can go a long way! Journal of Vocational Behavior, 121, 103474. https://doi.org/10.1016/j.jvb.2020.103474

Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups

and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209-213. https://doi.org/10.1016/j.jbusres.2020.05.036

Rahman, M.A., Zaman, N., Asyhari, A.T., Al-Turjman, F., Bhuiyan, M.Z.A., & Zolkipli, M.F. (2020). Data-driven dy-namic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustainable Cities and Society, 62, 102372. https://doi.org /10.1016/j.scs.2020.102372

Sawangchai, A., Prasarnkarn, H., Kasuma, J., Polyakova, A.G., & Qasim, S. (2020). Effects of COVID-19 on digital learning of entrepreneurs. Polish Journal of Management Studies, 22(2), 502-517. https://doi.org/10.17512/pjms.2020.22.2.33

Wach, K., & Bilan, S. (2021). Public support and administration barriers towards entrepreneurial intentions of students in Poland. Administratie si Management Public, 36(1).

Zandi, G., Shahzad, I., Farrukh, M., & Kot, S. (2020). Supporting role of society and firms to COVID-19 manage-ment among medical practitioners. International Journal of Environmental Research and Public Health, 17(21), 1-2. 7961. https://doi.org/10.3390/ijerph17217961

Zarikas, V., Poulopoulos, S.G., Gareiou, Z., & Zervas, E. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in Brief, 31, 105787. https://doi.org/10.1016/j.dib.2020.105787

Published : 2021-06-01

KinnunenJ., GeorgescuI., HosseiniZ., & AndroniceanuA.-M. (2021). Dynamic indexing and clustering of government strategies to mitigate Covid-19. Entrepreneurial Business and Economics Review, 9(2), 7-20. https://doi.org/10.15678/EBER.2021.090201

Jani Kinnunen  jpkinnunen@gmail.com
Åbo Akademi University, Turku, Finland  Finland

Doctoral Researcher at Information Systems, Åbo Akademi, Turku, Finland. His research interests include public policy analytics, machine learning, fuzzy modelling, and investment analysis.

Correspondence to: Jani Kinnunen, Savitehtaankatu 2 A 4, 20540, Turku, Finland, email: jani.kinnunen@abo.fi

Irina Georgescu 
Bucharest University of economics  Romania

PhD Lecturer at Department of Informatics and Economic Cybernetics (Bucharest University of Economic Studies, Bucharest, Romania). Her research interests include time series analysis and data mining applications.

Correspondence to: Irina Georgescu, PhD, Bucharest University of Economics, Calea Dorobantilor 15-17, Sector 1, Bucharest, 010552, Romania, e-mail: irina.georgescu@csie.ase.ro

Zahra Hosseini 
Tampere University  Finland

PhD in Educational Technology (2013, University of Malaya, Kuala Lumpur, Malaysia); PhD researcher in Communication and Media Department, Tampere University, Finland; Her research interests include TPACK, technology integration into teaching and learning, Technology Acceptance Models.

Correspondence to: Tampere University, Kalevantie 4, 33100 Tampere, Finland, email: zahra.hosseini@tuni.fi

Ane-Mari Androniceanu 
Management Doctoral School, Bucharest University of Economic Studies, Bucharest, Romania  Romania

PhD Student at the Management Doctoral School, Bucharest University of Economic Studies, Bucharest, Romania. Her research interests include: economic growth, digitalisation, circular economy, international business and entrepreneurship.

Correspondence to: ane.androniceanu.drd@gmail.com

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