ABSTRACT
BACKGROUND: Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. METHODS: The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. RESULTS: Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. CONCLUSION: Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.