Abstract
The coronavirus pandemic has posed many new, ambiguous and big challenges to people, business and governments at all levels - federal, provincial, and regional. In addition to the direct consequences from taking care of infected people, which in some countries and regions led to a virtual collapse of the healthcare system, the pandemic has further strained eldercare, employment and economic growth, and exacerbated mental health and social problems. During the first part of the pandemic, the main narrative focused on “flattening the curve” with many researchers providing forward looking predictions regarding the number of COVID-19 infections and deaths, and potential success of various strategies in curbing the spread of the virus. After a year of the pandemic, it is now time to look back, assess the numbers, and see what learning lessons there are. We present a non-parametric data-driven framework based on Data Envelopment Analysis to assess COVID-19 in eight Canadian provinces over the period March 2020 to March 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if things could have been worse and/or better. In order to take account for any indirect socio-economic impact our analysis incorporates monthly unemployment rates.
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