Epistemic preparedness

Area of Science:

• Global Health
• Epidemiology
• Data Science

Background:

• Traditional infectious disease preparedness emphasizes surveillance and medical resources.
• The COVID-19 pandemic highlighted limitations in current strategies, particularly concerning data practices and modeling.
• There's a need to explore cognitive and epistemic dimensions of preparedness.

Purpose of the Study:

• To reconceptualize preparedness in global health beyond 'crisis technologies'.
• To examine lessons learned from COVID-19 regarding data practices and statistical modeling.
• To propose a framework for epistemic preparedness in managing future disease outbreaks.

Main Methods:

• Analysis of data collection, sharing, and modeling practices during recent outbreaks.
• Inquiry into alternative data practices and complex epidemiological framings.
• Examination of colonial legacies and national limitations in epidemiological reasoning.

Main Results:

• Current preparedness strategies are often conceptually inflexible, relying on reductive 'crisis technologies'.
• There are significant challenges in data collection, sharing, and outbreak modeling.
• Addressing colonial legacies and national limitations is essential for equitable epidemiological reasoning.

Conclusions:

• Epistemic preparedness, focusing on cognitive and data-related aspects, is vital for future disease outbreak management.
• A more diverse, equitable, and inclusive approach to preparedness, encompassing both 'stocktaking' and 'stockpiling', is necessary.
• Reconfiguring our cognitive toolkit for global health challenges provides a reliable foundation for resilience.