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...
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...
As emerging pathogens frequently share biological similarities with previously circulating diseases, pre-existing vaccines may provide valuable yet imperfect protection during the early stages of an outbreak, before disease-specific vaccines become available.
In this work, we present a compartmental model that integrates two types of vaccines: a partially protective one, and a more...
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...
Individuals adapt to epidemics, creating a feedback loop between disease spread and human behavior. Incorporating this dynamic into infectious disease models is critical for evaluating interventions against future pandemic threats. We embedded an XGBoost algorithm, trained on real-world survey data (Imperial College London YouGov COVID-19 Behavior Tracker; Netherlands, Jun 2020โJan 2021;...
Google mobility data has been widely used in COVID-19 mathematical modeling to understand disease transmission dynamics. This review examines the extensive literature on the use of Google mobility data in COVID-19 mathematical modeling. We mainly focus on over a dozen influential studies using Google mobility data in COVID-19 mathematical modeling, including compartmental and metapopulation...
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...
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...
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...
