Speaker
Description
Mismatches between modelled and real-world outcomes frequently arise from misplaced assumptions about how people live, interact, and respond to infectious disease threats and interventions. Co-production is increasingly recognised as a way of ensuring that models reflect the realities of those most affected. Without it, models risk overlooking context, perpetuating inequities, and causing inadvertent harm, particularly among marginalised groups.
Meaningful co-production requires more than collaboration; it depends on methods, frameworks and values that enable the systematic integration of diverse forms of knowledge into the modelling process. Social science disciplines and methods that generate evidence on behaviour, social structure and wider cultural and political context provides a foundation for this work.
In my talk, I will introduce key social science approaches and frameworks including qualitative and participatory methods, and assumptions about knowledge showing how they can facilitate co-production. I will argue that social science helps create the conditions for inclusive co-production, supports its integration across all stages of modelling, and enables its evaluation. Drawing on examples from literature and from our own team’s work, I will demonstrate how social science strengthens both process and input, helping to ground models in the messy realities of the lives they seek to represent.
