Human behaviour can profoundly alter disease dynamics and the impact of public health interventions. Recognising this socio-epidemiological interplay, recent modelling efforts have increasingly incorporated behavioural responses triggered by perceived risk, disease outcomes, policy recommendations, or conformity pressure. We present a mathematical model that integrates two types of behavioural...
Understanding social interactions and the structure that arises from them is central to developing realistic epidemic models of many human pathogens. Surveys are often limited in size and limited in the information they can collect. Contact tracing, where contacts of cases are themselves traced and information collected, represents an idea way to collect social mixing information, but is...
Waterborne diseases continue to pose a major global public health concern, particularly in areas lacking adequate water infrastructure. During outbreaks, changes in human behavior often play a crucialโsometimes dominantโrole in shaping disease transmission. We introduce a reactionโdiffusion model that accounts for varying patterns of human mobility and behavioral responses within a spatially...
Social interactions and disease transmission are tightly intertwined, with behavioural responses and risk perception evolving during an epidemic. Capturing these feedbacks remains a major challenge in epidemic modeling. In social networks, the โegoโ denotes the focal individual, while โaltersโ are individuals directly connected to the ego. We propose a type-configuration algorithm based on...
While realistic approaches have become increasingly important in epidemic modelling, behavioral factors and individual differences have historically been overlooked due to the lack of high-resolution data and appropriate mathematical methods. This gap became particularly evident during the recent pandemic, highlighting the need for large-scale data collection on individual-level...
Epidemic dynamics are shaped not only by biological processes but also by how individuals perceive risk, adopt protective behaviours, and interact within socially structured populations. This talk explores how behavioural feedback and social homophily jointly influence the uptake of interventions and disease transmission dynamics. We will introduce a homophily-based modelling framework in...
Social distancing is now a familiar strategy for managing disease outbreaks, but it is important to understand the interaction between disease dynamics and social behaviour. We distinguished the fully susceptible from social-distancing susceptibles and proposed a Filippov epidemic model to study the effect of social distancing on the spread and control of infectious diseases. The threshold...
We present a comprehensive agent-based model designed to investigate the complex interplay between avoidant behavior and influenza transmission dynamics under varying levels of vaccine efficacy. The modelโs architecture is grounded in an age-stratified contact matrix and stochastic transmission probabilities, where individual health outcomes are determined by their specific disease history....
In a pandemic, alongside biological factors, societal interactions, cognitive behaviours, and personal attitudes can also influence the progression of an epidemic. For instance, people's compliance to vaccination or non-pharmaceutical measures rely on their social links as well as their individual opinions. How people connect with each other and their clustering structure also adds...
