Publication
Throughout history, pandemics have profoundly impacted societies, underscoring the need for effective public health interventions. The recent COVID-19 pandemic highlighted the importance of Non-Pharmaceutical Interventions (NPIs) in controlling disease transmission by altering individual behavior and mobility patterns. Testing preferences, self-quarantine decisions, risk assessments, and work-from-home options affect daily routines and the spread of disease. Understanding the complex interplay between mobility, behavior, and transmission is essential for refining public health strategies and reducing societal disruptions. This thesis addresses the integration of mobility patterns and behavior into epidemiological models to enhance intervention effectiveness.
Firstly, the thesis addresses the mobility pattern changes due to the trigger of a health crisis by introducing the Activity-Based Risk Perception Restriction Model, ABR2M. The ABR2M is an activity-based model that captures the impact of NPIs, such as curfews and activity closures, and the individuals' risk perception to perform specific activities to simulate daily activity patterns. Results show that curfews increase work time by 57%, while high perceived risk reduces secondary activities by 80%. These findings improve predictions of contact patterns and disease spread, aiding policymakers in designing effective responses. Secondly, it uses these mobility patterns to develop the Mobility-Aware Behavioral Epidemiological Model, MABEM. This epidemiological model connects the individual's daily scheduling choices with their testing behavior to predict disease dynamics. By incorporating behavior-driven testing, MABEM addresses underreporting issues, improving model accuracy and intervention effectiveness. Finally, the thesis presents a decision support tool that combines heterogeneous mobility patterns and disease dynamics to design targeted public health policies that balance health and economic outcomes. By optimizing interventions, the tool reduces disruptions while managing disease transmission effectively.
In summary, this thesis presents an interdisciplinary approach that combines mobility, behavioral dynamics, and epidemiology, supported by empirical data and simulations. The models and tools developed provide a robust foundation for understanding mobility changes triggered by crises, enabling policymakers to design public health policies that are both effective and minimally invasive. This work enhances readiness for future pandemics and contributes to more informed, adaptive responses to humanitarian crises.