Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

Area of Science:

• Epidemiology
• Data Science
• Public Health Policy

Background:

• Pandemics like COVID-19 have profound societal and economic impacts beyond health concerns.
• Standard containment measures like lockdowns, while necessary, incur significant economic costs.
• Existing strategies for pandemic control (vaccination, herd immunity, lockdown) have limitations.

Purpose of the Study:

• To propose a data-driven framework for moderating the adverse economic impact of COVID-19 outbreaks.
• To develop a dynamic clustering algorithm for implementing localized lockdowns.
• To balance pandemic control with economic sustainability.

Main Methods:

• Developed a data-driven dynamic clustering framework.
• Fused healthcare and simulated mobility data for analysis.
• Modeled lockdown as a clustering problem, designing a localized dynamic clustering algorithm.
• Validated the approach through extensive simulations using Malaysia as a case study.

Main Results:

• Demonstrated the effectiveness of dynamic clustering for reducing lockdown coverage.
• Showcased potential for significant economic loss reduction.
• Assessed the impact of uncooperative populations on contamination rates.
• Identified benefits in reduced military unit deployment.

Conclusions:

• Dynamic clustering offers a promising approach to mitigate the severe economic consequences of COVID-19.
• The framework can be adapted for future pandemic waves and other viral threats.
• Localized lockdowns informed by data can optimize public health interventions while minimizing economic disruption.