Modelling the pandemic: attuning models to their contexts.

Area of Science:

• Public Health
• Epidemiology
• Social Sciences in Health

Background:

• Mathematical models are crucial for pandemic policy but often detach from implementation contexts.
• Previous pandemics like Ebola and influenza highlight the need to integrate diverse research.
• The COVID-19 pandemic underscores the urgency of understanding model-context relationships.

Purpose of the Study:

• To analyze how sociological and anthropological research can improve pandemic modeling.
• To explore how models become disconnected from their implementation contexts.
• To propose an adaptive, open-model approach for pandemic response.

Main Methods:

• Case study analysis of Ebola and influenza pandemic modeling.
• Qualitative mapping of sociological and anthropological contributions to pandemic modeling.
• Theoretical exploration of adaptive and emergent scientific approaches.

Main Results:

• Pandemic models often disconnect from local contexts due to global response narratives.
• Sociological and anthropological research can re-contextualize models for specific situations.
• An adaptive, iterative modeling approach enhances relevance and effectiveness.

Conclusions:

• Integrating social sciences is vital for creating context-aware pandemic models.
• An open, deliberative approach to modeling fosters adaptive responses to uncertainty.
• This challenges traditional evidence-based global health by emphasizing emergent, adaptive science.