The exposure risk to COVID-19 in most affected countries: A vulnerability assessment model

Abstract

The world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries’ response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing.

Associations of Sociodemographic Factors and Health Behaviors with the Emotional Well-Being of Adolescents during the COVID-19 Pandemic in Brazil

“…Lastly, sociodemographic characteristics were the distal determinants, which may interfere in both the adoption of healthy behaviors and in emotional well-being [40]. In block 1, the following sociodemographic characteristics were considered: sex, age groups (12-15; 16-17 years old), skin color (white; non-white), mother's educational level (no college education, complete college education), family financial difficulties, and food insecurity ("Was there ever a concern that food would run out before your parents had money to buy more food?…”

“…During the period of social restraint measures, low-income families had more difficulties in facing the pandemic [40]. In Brazil, there was a greater reduction in family income among the poorest sections of the population, while informal workers were left without work, raising concerns about family maintenance and food insecurity [41][42][43].…”

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

“…There have been many pandemic studies, including COVID-19 risk assessment studies, which suggested different prediction and mitigation models. [2][3][4][5] However, all these models are comparable to extinguishing a fire, which, of course, must be done immediately in order to survive. In this paper, we explore a Pandemic Risk Exposure Measurement (PREM) model 1 that identifies factors that influence a country's perceived vulnerability to pandemic risk exposure such as COVID-19.…”

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

“…For example, a study predicted the infectious disease vulnerability index (IDVI) through an integrated modeling approach during the onset of coronavirus infection in Wuhan, China [ 77 , 78 , 79 ]. Multiple indicators such as travel information, country’s socio-economic condition, politics and health management facilities, and economic metrics are used to calculate IDVI scores [ 77 , 78 , 79 , 80 ]. IDVI scores range from 0 to 1, and a lower score signifies potential epidemic threats.…”