A recent article co-authored by Postdoctoral Fellow Dr Fanqi Zeng explores the difficulties of combining human behaviour with epidemiological modelling – tools used to predict how diseases spread.
The research, inspired by discussions at a workshop at the University of Warwick in June 2024, outlines the need for interdisciplinary collaboration to build robust ‘epidemiological-behavioural’ models.
These advanced models would integrate data on infection transmission with human behavioural dynamics, aiming to improve the accuracy of predictions during health crises like pandemics.
Human behaviour plays a pivotal role in how diseases spread, influencing both individual infection risks and community-level transmission. However, current models often fail to account for behaviours such as adherence to public health measures, societal norms, and the impact of policy changes.
For example, during the COVID-19 pandemic, gaps in modelling behavioural responses – like adherence to social distancing and mask mandates – highlighted the limitations of existing systems.
The paper identifies interdisciplinary collaboration as a critical factor in overcoming these challenges. Effective partnerships must bring together experts from behavioural, biological, data, mathematical, and social sciences, while fostering a shared language and standardised methods to bridge traditionally distanced disciplines.
Dr Zeng and colleagues also highlight the difficulties in predicting behavioural responses. While existing behavioural science models explain why and who might adopt certain behaviours, they struggle with forecasting when these behaviours will occur.
For example, during COVID-19, models identified factors influencing vaccine uptake but could not predict public reactions to sudden policy shifts.
Another issue is the lack of diverse, inclusive data. Historically, behavioural science research has relied heavily on data from students or populations in Western, industrialised countries, limiting its global applicability.
To address this, researchers advocate for more representative datasets, advanced real-time data collection methods such as sentiment analysis, and the inclusion of underrepresented groups.
Emerging technologies, like mobile apps and machine learning, offer new opportunities for real-time insights but raise ethical concerns around privacy and data security. Transparent policies and public trust are essential to ensure these tools are used responsibly.
The University of Liverpool's Dr Edward Hill, one of the main organisers of the workshop and the corresponding author, noted:
The COVID-19 pandemic has made clear the tight linkage between behavioural response, infectious disease transmission and infection outcomes.
Our study summarises four challenge areas in a developing, interdisciplinary research discipline that brings together the dynamics of human behaviour and epidemiological models.
The recommendations we pose can give entry points to research scientists, practitioners and policy makers for becoming involved in helping address these challenges.
The paper also underscores the importance of effective communication between scientists, policymakers, and the public. Simplifying complex findings without losing critical nuance, such as in debates over mask efficacy during COVID-19, remains a challenge.
Beyond public health, the findings have implications for veterinary and plant health, with potential applications in managing animal diseases, crop health, and tree disease outbreaks.
Dr Zeng concluded:
Epidemiological-behavioural models face significant challenges, but the growing interest in integrating behavioural realism into mathematical modelling is promising.
By bridging interdisciplinary gaps and contributing insights from social sciences, we can establish a new field of mathematical behavioural science, creating powerful tools to inform policy decisions and deliver evidence-based interventions for public, veterinary, and plant health.
