Policy analysis and data mining tools for controlling COVID-19 policies

Area of Science:

• Epidemiology
• Public Health Policy
• Data Science

Background:

• Despite extensive research on COVID-19 vaccine efficacy, real-world policy outcomes remain a critical concern.
• Assessing the effectiveness of diverse public health interventions during the pandemic is essential for informed decision-making.

Purpose of the Study:

• To introduce novel analytical tools for evaluating COVID-19 policy outcomes at both national and sub-national levels.
• To identify best practices in pandemic policy and highlight areas for improvement in scientific evidence-based research.

Main Methods:

• Development of three distinct analysis tools: jpscore (Japan prefectures), scorecovid (global countries), and hiscovid (time-series visualization).
• Utilization of a core metric: COVID-19 deaths per million population for policy scoring.
• Comparative analysis of policy effectiveness across different regions and timeframes.

Main Results:

• The developed tools provide objective scores to rank and compare COVID-19 policies worldwide and within Japan.
• Analysis indicates that poorly performing regions can adopt successful strategies from highly-scored counterparts.
• The findings underscore the importance of a sustainable, mandatory test-and-isolation strategy for pandemic mitigation.

Conclusions:

• The proposed tools offer valuable insights for policymakers to make data-driven decisions.
• Adoption of a global mandatory test-and-isolation strategy is recommended for effective pandemic control.
• Addressing gaps in scientific evidence-based research is vital for enhancing future pandemic response, particularly in Japan.