Intra-Provincial Benchmark Analysis on COVID-19 in Canada

Abstract

The COVID-19 pandemic has posed unheralded challenges to people, business, government (federal, provincial, regional), and society at large. In addition to the direct consequences from taking care of infected people, which for some countries and regions led to a virtual collapse of the healthcare system, the pandemic has strained eldercare, employment, 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 researchers forecasting COVID-19 infections and deaths, and the potential success of various strategies in curbing the virus. After almost two years of the pandemic, it is now time to look back, assess the numbers, and see what insights there are. We present a non-parametric data-driven framework based on Data Envelopment Analysis to assess COVID-19 in ten Canadian provinces over the period March, 2020, to November, 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if and to what extent the situation could have been worse respectively better. To take account for any indirect socio-economic impact our analysis incorporates official monthly unemployment rates and a stringency index reflecting the level of social policy restrictions imposed by the provincial governments.

Note: Funding Statement: This research was supported by Discovery Grants (of both Mehmet A. Begen and Fredrik Odegaard) from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Declaration of Interests: The authors declare that they have no conflict of interest.

Keywords: Coronavirus; COVID-19; Data Envelopment Analysis (DEA); Data Analytics; Undesirable Outputs; Canadian Provincial Healthcare