TY - JOUR
T1 - Efficacy of early warning systems in assessing country-level risk exposure to COVID-19
AU - Qazi, Abroon
AU - Simsekler, Mecit Can Emre
AU - Akram, Muhammad
N1 - Funding Information:
This publication is supported by the American University of Sharjah.
Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - COVID-19 has evolved as a pandemic causing unprecedented damages and disruptions to all spheres of life including healthcare, transportation, supply chains, education, and economy, among others. Pandemics are very low-probability events associated with deep uncertainty about the timing of such events and ensuing damages. National policy-makers generally rely on a set of risk indices associated with natural disasters and pandemics to assess the country’s vulnerability and strategy formulation for such rare events. This paper explores the efficacy of early warning systems (disasters and epidemics-based risk ratings) in predicting the country-level exposure to COVID-19. Utilizing three real data-sets reflecting the risk exposure of individual countries to disasters, epidemics, and COVID-19, we explore relations among the associated risk dimensions, namely hazard and exposure, vulnerability, and lack of coping capacity. A comprehensive methodology integrating Pearson’s correlation, ANOVA, and Bayesian Belief Networks-based techniques is adopted to explore and triangulate relations among the three risk indices. Results show that the risk ratings associated with epidemic risk and COVID-19 risk are statistically strongly correlated. However, only the vulnerability dimension of epidemic risk significantly influences the two risks.
AB - COVID-19 has evolved as a pandemic causing unprecedented damages and disruptions to all spheres of life including healthcare, transportation, supply chains, education, and economy, among others. Pandemics are very low-probability events associated with deep uncertainty about the timing of such events and ensuing damages. National policy-makers generally rely on a set of risk indices associated with natural disasters and pandemics to assess the country’s vulnerability and strategy formulation for such rare events. This paper explores the efficacy of early warning systems (disasters and epidemics-based risk ratings) in predicting the country-level exposure to COVID-19. Utilizing three real data-sets reflecting the risk exposure of individual countries to disasters, epidemics, and COVID-19, we explore relations among the associated risk dimensions, namely hazard and exposure, vulnerability, and lack of coping capacity. A comprehensive methodology integrating Pearson’s correlation, ANOVA, and Bayesian Belief Networks-based techniques is adopted to explore and triangulate relations among the three risk indices. Results show that the risk ratings associated with epidemic risk and COVID-19 risk are statistically strongly correlated. However, only the vulnerability dimension of epidemic risk significantly influences the two risks.
KW - ANOVA
KW - Bayesian Belief Networks
KW - COVID-19 risk
KW - disasters
KW - epidemics
KW - healthcare
KW - pandemic
KW - vulnerability
UR - http://www.scopus.com/inward/record.url?scp=85113189230&partnerID=8YFLogxK
U2 - 10.1080/19475705.2021.1962984
DO - 10.1080/19475705.2021.1962984
M3 - Article
AN - SCOPUS:85113189230
SN - 1947-5705
VL - 12
SP - 2352
EP - 2366
JO - Geomatics, Natural Hazards and Risk
JF - Geomatics, Natural Hazards and Risk
IS - 1
ER -