SCORECOVID: A Python Package Index for Scoring the Individual Policies Against COVID-19

This study proposes SCORECOVID, a new Python Package Index (PyPI) for scoring individual policies against covid-19 and mitigating the pandemic. The new PyPI package consists of two modules. The first module automatically scrapes the latest information on the number of deaths and population by COVID-19 to score individual policies for a given country. The second module calculates the score by dividing the number of deaths by the population in millions. The Federal Communications Commission (FCC) in the US estimates the economic value of a statistical life to be $9.5 million per individual. The higher the number of deaths, the greater the economic loss. To use the best policies to reduce the number of deaths, we should adopt measures and methods from exceptional countries with high scores. The proposed method reveals two groups: a high-scored group and a low-scored group. The number of deaths is an indicator of economic and health policy scores. SCORECOVID is the world's first open-source policy scoring tool for COVID-19. It is designed to help many countries utilize state-of-the-art analytics methods to effectively mitigate the COVID-19 pandemic.