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Background: To understand the impact and volume of coronavirus (COVID-19) crisis, univariate analysis is tedious for describing the datasets reported daily. However, to capture the full picture and be able to compare situations and consequences for different countries, multivariate analytical models are suggested in order to visualize and compare the situation of different countries more accurately and precisely. Aims: We aimed to utilize data analysis tools that display the relative positions of data points in fewer dimensions while keeping the variation of the original data set as much as possible, and cluster countries according to their scores on the formed dimensions. Methods: Principal component analysis (PCA) and Partitioning around medoids (PAM) clustering algorithms were used to analyze data of 56 countries, 82 countries and 91 countries with COVID-19 at three time points, eligible countries included in the analysis are those with total cases of 500 or more with no missing data. Results: After performing PCA, we generated two scores: Disease Magnitude score that represents total cases, total deaths, total actives cases, and critically ill cases, and Mortality Recovery Ratio score that represents the ratio between total deaths to total recoveries in any given country. Conclusion: Accurate multivariate analyses can be of great value as they can simplify difficult concepts, explore and communicate findings from health datasets, and support the decision-making process.

The key issue of this current study is related to shaping the attractiveness of heritage destinations, highlighting the significance of reuse and upgrading their historical buildings to achieve a high level of competitiveness and distinctiveness through a smart approach. Some of these cultural assets and events tend to be monotonous, and not so attractive for various categories of tourists, which negatively affects investment opportunities, tourism development, and social and economic resources. Furthermore, previous works have criticized the lack of evidence to support that the structure contains critical attributes and measurement items linked to the competitiveness of smart heritage destinations. As a result, this study aims to design and develop a composite index for evaluating these destinations and their buildings, which includes nine dimensions (attributes) and a set of key performance indicators (KPIs) of intelligent performance and competitiveness, reflecting the combination and noticing the distinct perspective between them. A mixed-methods approach was used between qualitative and quantitative methods to perform content validation on the proposed index. Furthermore, a pilot study was implemented for tourism heritage destinations to improve the quality and efficiency of the proposed index. Then, exploratory factor analysis (EFA) was used to analyze the data to develop the proposed index and measure its validity and reliability. Finally, the proposed composite index was finalized with 139 KPIs and applied to a case study (Salah El-Din Citadel). After that, we validated its utility in providing a quantitative evaluation of this heritage destination, identifying critical intervention priorities, and determining dimensions that need to be restructured. Additionally, it highlighted recommendations for future improvements to strengthen these heritage destinations to become smart heritage destinations capable of competition in the tourism sector.

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