SCORECOVID: A Python Package Index for scoring the individual policies against COVID-19

Area of Science:

• Public Health
• Health Policy Analysis
• Data Science

Background:

• The COVID-19 pandemic necessitated rapid development and evaluation of public health policies.
• Assessing the effectiveness of diverse national strategies against COVID-19 remains a critical challenge.
• Economic implications of mortality, such as the value of statistical life, underscore the importance of effective mitigation.

Purpose of the Study:

• To introduce SCORECOVID, a novel Python Package Index (PyPI) tool for scoring COVID-19 policies.
• To provide a quantitative method for evaluating and comparing national pandemic mitigation efforts.
• To facilitate the adoption of successful strategies by identifying high-performing countries.

Main Methods:

• Development of SCORECOVID, a two-module Python package.
• Module 1: Automated scraping of COVID-19 deaths and population data.
• Module 2: Calculation of a policy score by dividing deaths by population in millions.

Main Results:

• The scoring method categorizes countries into high-scored and low-scored groups.
• The number of deaths serves as a key indicator for economic and health policy effectiveness.
• Identified high-scoring countries can serve as models for effective pandemic mitigation.

Conclusions:

• SCORECOVID offers the world's first open-source policy scoring tool for COVID-19.
• The tool enables data-driven decision-making for pandemic response.
• Utilizing advanced analytics, SCORECOVID aids countries in mitigating the COVID-19 pandemic effectively.