Factors that most expose countries to COVID-19: a composite indicators-based approach

Abstract

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries’ exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R > 0.60) with composite indicator scores and moderately concordant ( K > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population’s life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population’s life expectancy and the country’s exposure to COVID-19.

Socio-Economic Vulnerability Assessment for Supporting a Sustainable Pandemic Management in Austria

“…Additionally, the categories Demography and the Health Condition had a high impact on the VI. Libório et al [52] used a composite indicator approach to estimate the exposure of countries to COVID-19 and established that age had the highest influence on the exposure to COVID-19. Hence, this is the driving factor for vulnerability on a countrywide scale.…”

“…Here, Health Conditions and Health Facilities exhibited a higher influence. One possible explanation might be that we analyzed age independently of health conditions, which could also be summarized in one category [52].…”

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

“…A simple tool to measure the economic progress of a country/region is given the per capita GDP. Among the 57 studies dealing with this variable, 35 (61.4%) revealed that the higher the gross domestic product per capita, the higher the rate of infection ( Varkey, Joy, Sarmah, & Panda, 2021 ; Baser, 2021 ; Libório, Ekel, de Abreu, & Laudares, 2021 ). Caution is needed in interpreting findings.…”

Socio-economic analysis of short-term trends of COVID-19: modeling and data analytics

“…In the existing literature, it was notified that the enhancement of the previous heath policy indices (ICU, P&C, Hospitals number, and the 4 T's) implies the reduction of the pandemic infection and death count. Even in the case of multidimensional analysis, the health policy was not speculated nor discussed from a wider perspective that could include the other socio-economic factors [62][63][64][65][66][67][68]. The age structure was also identified to have significant effect on the infection evolution, while elderly is assumed to be highly correlated to death cases; an exponential relation was denoted [57,59].…”