The SARS-CoV-2 pandemic highlighted that epidemic models fail to incorporate data-driven and theoretical knowledge of behavioural response to pandemics. This gap is partially driven by the lack of quantitative models that can predict the adoption of behaviours across individuals and populations, particularly in new social contexts. Hence, there is a need to improve behavioural realism in...
Standard epidemic models tend to assume that human behaviour is fixed, rational, or slow to change. Reality is messier. During COVID-19 and beyond, behaviour has proven fast-moving, socially contagious, and often emotionally driven. People respond to risk, to each other, and to policy - sometimes amplifying interventions, sometimes undermining them. We present a modelling framework that treats...
Peer influence can act as an invisible force that promotes or hinders the adoption of health-protective behaviours. While traditional epidemiological models focus on physical contacts driving transmission, they often overlook the social interactions through which opinions and behaviours spread.
We use contact data from a multinational survey of over 22,000 respondents across six European...
Annual vaccination remains the most effective intervention in reducing the burden of seasonal influenza epidemics. Considering vaccination strategies over multiple seasons could allow the identification of more effective interventions than those designed for a single season. The effect of vaccination depends on and changes both the immunological memory of the host population and the...
Reducing the harmful impact of pesticides is a key challenge faced by the agricultural industry globally. In many countries, policies have been introduced to discourage the use of pesticides, while encouraging the use of alternative methods of crop disease control. One such method is Integrated Pest Management (IPM), a set of holistic and sustainable measures for the prevention, monitoring and...
Accurately capturing epidemic dynamics requires accounting for how individuals adjust behaviour in response to perceived infection risk. Recently models have been developed to incorporate behavioural feedback \cite{ward_bayesian_2023}, but they currently rely on simplified, ad hoc representations of memory. For instance, many emphasize only recent case counts, neglecting the lasting influence...
Spontaneous behavioural changes in response to an outbreak can significantly alter transmission dynamics. Recent studies \cite{ Omori2024JTB} have explored how the human perception of risk, and the subsequent reduction in contact rates, can be modelled as a function of epidemiological indicators. This study aims to provide a qualitative understanding of how such perception-based feedback...
Transmission models are used to predict the course of epidemics and inform policy makers. For respiratory diseases, many models incorporate rates of contacts within and between age groups, summarized in contact matrices. Contact rates may change due to control measures. To forecast transmission, contact matrices should reflect this impact before measures are introduced. We present a protocol...
The behavioural element in the transmission dynamics of infectious diseases is very influential; infection risk affects behaviour while behaviour affects individual and communal infection risk.
There are several challenges recognised amongst the epidemiology and infectious disease modelling communities on incorporating the dynamics of behaviour into models of infectious disease dynamics...
