A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data

Simon Grima; Ramona Rupeika-Apoga; Murat Kizilkaya; Inna Romānova; Rebecca Dalli Gonzi; Mihajlo Jakovljevic

doi:10.2147/RMHP.S341500

A Proactive Approach to Identify the Exposure Risk to COVID-19: Validation of the Pandemic Risk Exposure Measurement (PREM) Model Using Real-World Data

Risk Manag Healthc Policy. 2021 Nov 26:14:4775-4787. doi: 10.2147/RMHP.S341500. eCollection 2021.

Authors

Simon Grima¹, Ramona Rupeika-Apoga², Murat Kizilkaya³, Inna Romānova², Rebecca Dalli Gonzi⁴, Mihajlo Jakovljevic^{5

6}

Affiliations

¹ Department of Insurance, Faculty of Economics, Management and Accountancy, University of Malta, Msida, Malta.
² Faculty of Business, Management and Economics, University of Latvia, Riga, Latvia.
³ Department of Economics, Faculty of Economics and Administrative Sciences, Ardahan University, Ardahan, Turkey.
⁴ Department of Construction & Property Management, University of Malta, Msida, MSD, 2080, Malta.
⁵ Institute of Comparative Economic Studies ICES, Faculty of Economics, Hosei University, Tokyo, Japan.
⁶ Department of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia.

Abstract

Purpose: To statistically validate the PREM (Pandemic Risk Exposure Measurement) model devised in a previous paper by the authors and determine the model's relationship with the level of current COVID-19 cases (NLCC) and the level of current deaths related to COVID-19 (NLCD) based on the real country data.

Methods: We used perceived variables proposed in a previous study by the same lead authors and applied the latest available real data values for 154 countries. Two endogenous real data variables (NLCC) and (NLCD) were added. Data were transformed to measurable values using a Likert scale of 1 to 5. The resulting data for each variable were entered into SPSS (Statistical Package for the Social Sciences) version 26 and Amos (Analysis of a Moment Structures) version 21 and subjected to statistical analysis, specifically exploratory factor analysis, Cronbach's alpha and confirmatory factor analysis.

Results: The results obtained confirmed a 4-factor structure and that the PREM model using real data is statistically reliable and valid. However, the variable Q14 - hospital beds available per capita (1000 inhabitants) had to be excluded from the analysis because it loaded under more than one factor and the difference between the factor common variance was less than 0.10. Moreover, its Factor 1 and Factor 3 with NLCC and Factor 1 with NLCD showed a statistically significant relationship.

Conclusion: Therefore, the developed PREM model moves from a perception-based model to reality. By proposing a model that allows governments and policymakers to take a proactive approach, the negative impact of a pandemic on the functioning of a country can be reduced. The PREM model is useful for decision-makers to know what factors make the country more vulnerable to a pandemic and, if possible, to manage or set tolerances as part of a preventive measure.

Keywords: COVID-19; PREM; pandemic risk exposure; proactive; vulnerability.

Grants and funding

Underlying research efforts behind this contribution have been partially funded through Grant Em-CEAS of The Science Fund of the Republic of Serbia and Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia.