Disease spread modeling using contact network - Wu, JunYan (Nanyang Technological University, 2023)
In recent years, due to emergent concerns from the COVID-19 pandemic, there has been an upsurge in interest surrounding the modeling of infectious disease transmission. One widely used approach relies on compartment models, which make assumptions of uniformly mixed populations. While these models offer insights on a macro level, making them suitable for broad analyses, they can fall short in capturing the intricate processes of individual-to-individual contacts that drive disease propagation. To address this gap, our final year project delves into predicting disease transmission using contact networks, employing the dataset titled “Contact Patterns in a High School: A comparison between Data Collected Using Wearable Sensors, Contact Diaries, and Friendship Surveys”. Initially, the project entails an exploratory data analysis to decipher the publicly available data and to visualize space-time activity patterns. Subsequent steps involve extracting a contact network from the dataset to depict disease transmission pathways through vertices and edges. The research then shifts to an in-depth exploration of the contact network's attributes, it was discerned that certain nodes, potentially 'super-spreaders', played a disproportionate role in potential transmission pathways. Our findings underscore the importance of micro-level analyses for informed intervention strategies. Recognizing high-risk individuals and understanding their interaction patterns can equip health authorities with a more granular toolkit, ultimately enabling more targeted and effective containment measures in outbreak scenarios.